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STAT 6710
Mathematical Statistics I
Fall Semester 1999
Dr. J�urgen Symanzik
Utah State University
Department of Mathematics and Statistics
3900 Old Main Hill
Logan, UT 84322{3900
Tel.: (435) 797{0696
FAX: (435) 797{1822
e-mail: [email protected]
Contents
Acknowledgements 1
1 Axioms of Probability 1
1.1 �{Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Manipulating Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Combinatorics and Counting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.4 Conditional Probability and Independence . . . . . . . . . . . . . . . . . . . . . . . . 16
2 Random Variables 23
2.1 Measurable Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2 Probability Distribution of a Random Variable . . . . . . . . . . . . . . . . . . . . . 27
2.3 Discrete and Continuous Random Variables . . . . . . . . . . . . . . . . . . . . . . . 31
2.4 Transformations of Random Variables . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3 Moments and Generating Functions 42
3.1 Expectation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.2 Generating Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.3 Complex{Valued Random Variables and Characteristic Functions . . . . . . . . . . . 57
3.4 Probability Generating Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.5 Moment Inequalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4 Random Vectors 72
4.1 Joint, Marginal, and Conditional Distributions . . . . . . . . . . . . . . . . . . . . . 72
4.2 Independent Random Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.3 Functions of Random Vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.4 Order Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.5 Multivariate Expectation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.6 Multivariate Generating Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
4.7 Conditional Expectation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4.8 Inequalities and Identities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
1
5 Particular Distributions 113
5.1 Multivariate Normal Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
5.2 Exponential Familty of Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
6 Limit Theorems 122
6.1 Modes of Convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
6.2 Weak Laws of Large Numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
6.3 Strong Laws of Large Numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
2
Acknowledgements
I would like to thank my students, Hanadi B. Eltahir, Rich Madsen, and Bill Morphet, who helped
in typesetting these lecture notes using LATEX and for their suggestions how to improve some of the
material presented in class.
In addition, I particularly would like to thank Mike Minnotte and Dan Coster, who previously
taught this course at Utah State University, for providing me with their lecture notes and other
materials related to this course. Their lecture notes, combined with additional material from
Rohatgi (1976) and other sources listed below, form the basis of the script presented here.
The textbook required for this class is:
� Rohatgi, V. K. (1976): An Introduction to Probability Theory and Mathematical Statistics,
John Wiley and Sons, New York.
A Web page dedicated to this class is accessible at:
http://www.math.usu.edu/~symanzik/teaching/1999_stat6710/stat6710.html
This course closely follows Rohatgi (1976) as described in the syllabus. Additional material origi-
nates from the lectures from Professors Hering, Trenkler, and Gather I have attended while study-
ing at the Universit�at Dortmund, Germany, the collection of Masters and PhD Preliminary Exam
questions from Iowa State University, Ames, Iowa, and the following textbooks:
� Bandelow, C. (1981): Einf�uhrung in die Wahrscheinlichkeitstheorie, Bibliographisches Insti-
tut, Mannheim, Germany.
� Casella, G., and Berger, R. L. (1990): Statistical Inference, Wadsworth & Brooks/Cole, Paci�c
Grove, CA.
� Fisz, M. (1989): Wahrscheinlichkeitsrechnung und mathematische Statistik, VEB Deutscher
Verlag der Wissenschaften, Berlin, German Democratic Republic.
� Kelly, D. G. (1994): Introduction to Probability, Macmillan, New York, NY.
� Mood, A. M., and Graybill, F. A., and Boes, D. C. (1974): Introduction to the Theory of
Statistics (Third Edition), McGraw-Hill, Singapore.
� Parzen, E. (1960): Modern Probability Theory and Its Applications, Wiley, New York, NY.
� Searle, S. R. (1971): Linear Models, Wiley, New York, NY.
3
Additional de�nitions, integrals, sums, etc. originate from the following formula collections:
� Bronstein, I. N. and Semendjajew, K. A. (1985): Taschenbuch der Mathematik (22. Au age),
Verlag Harri Deutsch, Thun, German Democratic Republic.
� Bronstein, I. N. and Semendjajew, K. A. (1986): Erg�anzende Kapitel zu Taschenbuch der
Mathematik (4. Au age), Verlag Harri Deutsch, Thun, German Democratic Republic.
� Sieber, H. (1980): Mathematische Formeln | Erweiterte Ausgabe E, Ernst Klett, Stuttgart,
Germany.
J�urgen Symanzik, January 18, 2000
4
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 2: Wednesday 9/1/1999
Scribe: J�urgen Symanzik
1 Axioms of Probability
1.1 �{Fields
Let be the sample space of all possible outcomes of a chance experiment. Let ! 2 (or x 2 )
be any outcome.
Example:
Count # of heads in n coin tosses. = f0; 1; 2; : : : ; ng.
Any subset A of is called an event.
For each event A � , we would like to assign a number (i.e., a probability). Unfortunately, we
cannot always do this for every subset of .
Instead, we consider classes of subsets of called �elds and �{�elds.
De�nition 1.1.1:
A class L of subsets of is called a �eld if 2 L and L is closed under complements and �nite
unions, i.e., L satis�es
(i) 2 L
(ii) A 2 L =) AC 2 L
(iii) A;B 2 L =) A [B 2 L
Since C = �, (i) and (ii) imply � 2 L. Therefore, (i)': � 2 L [can replace (i)].
Recall De Morgan's Laws:[A2A
A = (\A2A
AC)C and
\A2A
A = ([A2A
AC)C :
1
Note:
So (ii), (iii) imply (iii)': A;B 2 L =) A \B 2 L [can replace (iii)].
Proof:
A;B 2 L(ii)=) A
C; B
C 2 L(iii)=) (AC [BC) 2 L
(ii)=) (AC [BC)C 2 L
DM=) A \B 2 L
De�nition 1.1.2:
A class L of subsets of is called a �{�eld (Borel �eld, �{algebra) if it is a �eld and closed under
countable unions, i.e.,
(iv) fAng1n=1 2 L =)1[n=1
An 2 L.
Note:
(iv) implies (iii) by taking An = � for n � 3.
Example 1.1.3:
For some , let L contain all �nite and all co�nite sets (A is co�nite if AC is �nite). Then L is a
�eld. But L is a �{�eld i� (if and only if) is �nite.
Example:
= Z. Take An = fng, each �nite, so An 2 L. But1[n=1
An = Z+ 62 L, since the set is not �nite (it
is in�nite) and also not co�nite ((1[n=1
An)C = Z
�0 is in�nite, too).
Question: Does this construction work for = Z+ ??
The largest �{�eld in is the power set P() of all subsets of . The smallest �{�eld is L = f�;g.
Terminology:
A set A 2 L is said to be \measurable L".
2
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 3: Friday 9/3/1999
Scribe: J�urgen Symanzik
We often begin with a class of sets, say a, which may not be a �eld or a �{�eld.
De�nition 1.1.4:
The �{�eld generated by a, �(a), is the smallest �{�eld containing a, or the intersection of all
�{�elds containing a.
Note:
(i) Such �{�elds containing a always exist (e.g., P()), and (ii) the intersection of an arbitrary #
of �{�elds is always a �{�eld.
Proof:
(ii) Suppose L =\�
L�. We have to show that conditions (i) and (ii) of Def. 1.1.1 and (iv) of Def.
1.1.2 are ful�lled:
(i) 2 L� 8� =) 2 L
(ii) Let A 2 L =) A 2 L� 8� =) AC 2 L� 8� =) A
C 2 L
(iv) Let An 2 L 8n =) An 2 L� 8� 8n =)[n
An 2 L� 8� =)[n
An 2 L
Example 1.1.5:
= f0; 1; 2; 3g; a = ff0gg; b = ff0g; f0; 1gg.
What is �(a)?
�(a): must include ;�; f0g
also: f1; 2; 3g by 1.1.1 (ii)
Since all unions are included, we have �(a) = f;�; f0g; f1; 2; 3gg
What is �(b)?
�(b): must include ;�; f0g; f0; 1g
also: f1; 2; 3g; f2; 3g by 1.1.1 (ii)
f0; 2; 3g by 1.1.1 (iii)
3
f1g by 1.1.1 (ii)
Since all unions are included, we have �(b) = f;�; f0g; f1g; f0; 1g; f2; 3g; f0; 2; 3g; f1; 2; 3gg
If is �nite or countable, we will usually use L = P(). If j j= n <1, then j L j= 2n.
If is uncountable, P() may be too large to be useful and we may have to use some smaller �{�eld.
De�nition 1.1.6:
If = IR, an important special case is the Borel �{�eld, i.e., the �{�eld generated from all
half{open intervals of the form (a; b], denoted B or B1. The sets of B are called Borel sets.
The Borel �{�eld on IRd (Bd) is the �{�eld generated by d{dimensional rectangles of the form
f(x1; x2; : : : ; xd) j ai < xi � bi; i = 1; 2; : : : ; dg.
Note:
B contains all points: fxg =1\n=1
(x� 1
n; x]
closed intervals: [x; y] = (x; y] + fxg = (x; y] [ fxgopen intervals: (x; y) = (x; y]� fyg = (x; y] \ fygC
and semi{in�nite intervals: (x;1) =1[n=1
(x; x+ n]
We now have a measurable space (; L). We next de�ne a probability measure P (�) on (; L) to
obtain a probability space (; L; P ).
De�nition 1.1.7: Kolmogorov Axioms of Probability
A probability measure (pm), P , on (; L) is a set function P : L! IR satisfying
(i) 0 � P (A) 8A 2 L
(ii) P () = 1
(iii) If fAng1n=1 are disjoint sets in L and1[n=1
An 2 L, then P (1[n=1
An) =1Xn=1
P (An).
Note:1[n=1
An 2 L holds automatically if L is a �{�eld but it is needed as a precondition in the case that
L is just a �eld. Property (iii) is called countable additivity.
4
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 4: Wednesday 9/8/1999
Scribe: J�urgen Symanzik, Bill Morphet
1.2 Manipulating Probability
Theorem 1.2.1:
For P a pm on (; L), it holds:
(i) P (�) = 0
(ii) P (AC) = 1� P (A) 8A 2 L
(iii) P (A) � 1 8A 2 L
(iv) P (A [B) = P (A) + P (B)� P (A \B) 8A;B 2 L
(v) If A � B, then P (A) � P (B).
Proof:
(i) An � ; 8n)1[n=1
An = ; 2 L.
Ai \Aj = ; \ ; = ; 8i; j ) An are disjoint 8n.
P (;) = P (1[n=1
An)Def1:1:7(iii)
=1Xn=1
P (An) =1Xn=1
P (;)
This can only hold if P (;) = 0.
(ii) A1 � A;A2 � AC; An � ; 8n � 3.
=1[n=1
An = A1 [A2 [1[n=3
An = A1 [A2 [ ;.
A1 \A2 = A1 \ ; = A2 \ ; = ; ) A1; A2; ; are disjoint.
1 = P () = P (1[n=1
An)
Def1:1:7(iii)=
1Xn=1
P (An)
5
= P (A1) + P (A2) +1Xn=3
P (An)
Th1:2:1(i)= P (A1) + P (A2)
= P (A) + P (AC)
=) P (AC) = 1� P (A) 8A 2 L.
(iii) By Th. 1.2.1 (ii) P (A) = 1�P (AC) =) P (A) � 1 8A 2 L since P (AC) � 0 by Def 1.1.7 (i)
(iv) A [ B = (A \ BC) [ (A \ B) [ (B \ A
C). So, (A [ B) can be written as a union of disjoint
sets (A \BC); (A \B); (B \AC):
=) P (A [B) = P ((A \BC) [ (A \B) [ (B \AC))
Def:1:1:7(iii)= P (A \BC) + P (A \B) + P (B \AC)
= P (A \BC) + P (A \B) + P (B \AC) + P (A \B)� P (A \B)
= (P (A \BC) + P (A \B)) + (P (B \AC) + P (A \B))� P (A \B)
= P (A) + P (B)� P (A \B)
(v) B = (B \AC) [A where (B \AC) and A are disjoint sets.
P (B) = P ((B \AC) [A) Def1:1:7(iii)= P (B \AC) + P (A)
=) P (A) = P (B)� P (B \AC)
=) P (A) � P (B) since P (B \AC) � 0 by Def 1.1.7 (i)
Theorem 1.2.2: Principle of Inclusion{Exclusion
Let A1; A2; : : : ; An 2 L. Then
P (n[
k=1
Ak) =nX
k=1
P (Ak)�nX
k1<k2
P (Ak1 \Ak2)+nX
k1<k2<k3
P (Ak1 \Ak2 \Ak3)� : : :+(�1)n+1P (
n\k=1
Ak)
Proof:
n = 1 is trivial
n = 2 is Theorem 1.2.1 (iv)
use induction for higher n (Homework)
Theorem 1.2.3: Bonferroni's Inequality
Let A1; A2; : : : ; An 2 L. Then
nXi=1
P (Ai)�Xi<j
P (Ai \Aj) � P (n[i=1
Ai) �nXi=1
P (Ai)
6
Proof:
Right side induction base:
For n = 1, Th. 1.2.3 right side evaluates to P (A1) � P (A1), which is true.
For n = 2, Th. 1.2.3 right side evaluates to P (A1 [A2) � P (A1) + P (A2).
P (A1 [A2)Th:1:2:1(iv)
= P (A1)+P (A2)�P (A1 \A2) � P (A1)+P (A2) since P (A1 \A2) � 0 by Def.
1.1.7 (i).
This establishes the induction base for the right side of Th. 1.2.3.
Right side induction step assumes Th. 1.2.3 right side is true for n and shows that it is true for
n+ 1:
P (n+1[i=1
Ai) = P ((n[i=1
Ai) [An+1)
Th:1:2:1(iv)= P (
n[i=1
Ai) + P (An+1)� P ((n[i=1
Ai) \An+1)
Def:1:1:7(i)
� P (n[i=1
Ai) + P (An+1)
I:B:�
nXi=1
P (Ai) + P (An+1)
=n+1Xi=1
P (Ai)
Left side induction base:
For n = 1, Th. 1.2.3 left side evaluates to P (A1) � P (A1), which is true.
For n = 2, Th. 1.2.3 left side evaluates to P (A1) + P (A2) � P (A1 \ A2) � P (A1 [ A2), which is
true by Th. 1.2.1 (iv).
For n = 3, Th. 1.2.3 left side evaluates to
P (A1) + P (A2) + P (A3)� P (A1 \A2)� P (A1 \A3)� P (A2 \A3) � P (A1 [A2 [A3).
7
This holds since
P (A1 [A2 [A3)
= P ((A1 [A2) [A3)
Th:1:2:1(iv)= P (A1 [A2) + P (A3)� P ((A1 [A2) \A3)
= P (A1 [A2) + P (A3)� P ((A1 \A3) [ (A2 \A3))
Th:1:2:1(iv)= P (A1) + P (A2)� P (A1 \A2) + P (A3)� P (A1 \A3)� P (A2 \A3)
+P ((A1 \A3) \ (A2 \A3))
= P (A1) + P (A2) + P (A3)� P (A1 \A2)� P (A1 \A3)� P (A2 \A3) + P (A1 \A2 \A3)
Def1:1:7(i)
� P (A1) + P (A2) + P (A3)� P (A1 \A2)� P (A1 \A3)� P (A2 \A3)
This establishes the induction base for the left side of Th. 1.2.3.
Left side induction step assumes Th. 1.2.3 left side is true for n and shows that it is true for n+1:
P (n+1[i=1
Ai) = P ((n[i=1
Ai) [An+1)
= P (n[i=1
Ai) + P (An+1)� P ((n[i=1
Ai) \An+1)
left I:B:
�nXi=1
P (Ai)�nXi<j
P (Ai \Aj) + P (An+1)� P ((n[i=1
Ai) \An+1)
=n+1Xi=1
P (Ai)�nXi<j
P (Ai \Aj)� P (n[i=1
(Ai \An+1))
Th:1:2:3 right side
�n+1Xi=1
P (Ai)�nXi<j
P (Ai \Aj)�nXi=1
P (Ai \An+1)
=n+1Xi=1
P (Ai)�n+1Xi<j
P (Ai \Aj)
8
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 5: Friday 9/10/1999
Scribe: J�urgen Symanzik, Rich Madsen
Theorem 1.2.4: Boole's Inequality
Let A;B 2 L. Then
(i) P (A \B) � P (A) + P (B)� 1
(ii) P (A \B) � 1� P (AC)� P (BC)
Proof:
Homework
De�nition 1.2.5: Continuity of sets
For a sequence of sets fAng1n=1; An 2 L and A 2 L, we say
(i) An " A if A1 � A2 � A3 � : : : and A =1[n=1
An.
(ii) An # A if A1 � A2 � A3 � : : : and A =1\n=1
An.
Theorem 1.2.6:
If fAng1n=1; An 2 L and A 2 L, then limn!1P (An) = P (A) if 1.2.5 (i) or 1.2.5 (ii) holds.
Proof:
Part (i): Assume that 1.2.5 (i) holds.
Let B1 = A1 and Bk = Ak �Ak�1 = Ak \ACk�1 8k � 2
By construction, Bi \Bj = ; for i 6= j
It is A =1[n=1
An =1[n=1
Bn
and also An =n[i=1
Ai =n[i=1
Bi
9
P(A) = P(1[k=1
Bk )By Def. 1.1.7 (iii)
=1Xk=1
P (Bk) = limn!1[
nXk=1
P (Bk)]
again by Def. 1.1.7 (iii)= lim
n!1[P (n[
k=1
Bk)] = limn!1[P (
n[k=1
Ak)] = limn!1P (An)
The last step is possible since An =n[
k=1
Ak
Part (ii): Assume that 1.2.5 (ii) holds.
Then, AC1 � A
C2 � A
C3 � : : : and A
C = (1\n=1
An)C DeMorgan
=1[n=1
ACn
P (AC)By Part (i)
= limn!1P (AC
n )
So 1� P (AC) = 1� limn!1P (AC
n )
=) P (A) = limn!1(1� P (AC
n )) = limn!1P (An)
Theorem 1.2.7:
(i) Countable unions of probability 0 sets have probability 0.
(ii) Countable intersections of probability 1 sets have probability 1.
Proof:
Part (i):
Let fAng1n=1 2 L, P (An) = 0 8n
0By K.A.P. (i)
� P (1[n=1
An)By Bonferroni's Inequality
�1Xn=1
P (An) =1Xn=1
0 = 0
Therefore P (1[n=1
An) = 0
Part (ii):
Let fAng1n=1 2 L, P (An) = 1 8n
by Th. 1.2.1 (ii)=) P (AC
n ) = 0 8n by Th. 1.2.7 (i)=) P (
1[n=1
ACn ) = 0
De Morgan=) P (
1\n=1
An) = 1
10
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 6: Monday 9/13/1999
Scribe: J�urgen Symanzik, Hanadi B. Eltahir
1.3 Combinatorics and Counting
For now, we restrict ourselves to sample spaces containing a �nite number of points.
Let = f!1; : : : ; !ng and L = P(). For any A 2 L;P (A) =X!j2A
P (!j).
De�nition 1.3.1:
We say the elements of are equally likely (or occur with uniform probability) if
P (!j) =1n 8j = 1; : : : ; n.
Note:
If this is true, P (A) =number !j in A
number !j in . Therefore, to calculate such probabilities, we just need to
be able to count elements accurately.
Theorem 1.3.2: Fundamental Theorem of Counting
If we wish to select one element (a1) out of n1 choices, a second element (a2) out of n2 choices, and
so on for a total of k elements, there are
n1 � n2 � n3 � : : : � nk
ways to do it.
Proof: (By Induction)
Induction Base:
k = 1: trivial
k = 2: n1 ways to choose a1. For each, n2 ways to choose a2.
Total # of ways = n2 + n2 + : : :+ n2| {z }n1 times
= n1 � n2.
Induction Step:
Suppose it is true for (k � 1). We show that it is true for k = (k � 1) + 1.
There are n1 � n2 � n3 � : : : � nk�1 ways to select one element (a1) out of n1 choices, a second
element (a2) out of n2 choices, and so on, up to the (k � 1)th element (ak�1) out of nk�1 choices.
For each of these n1 � n2 � n3 � : : : � nk�1 possible ways, we can select the kth element (ak) out
of nk choices. Thus, the total # of ways = (n1 � n2 � n3 � : : : � nk�1)� nk.
11
De�nition 1.3.3:
For positive integer n, we de�ne n factorial as n! = n� (n�1)� (n�2)� : : :�2�1 = n� (n�1)!
and 0! = 1.
De�nition 1.3.4:
For nonnegative integers n � r, we de�ne the binomial coe�cient (read as n choose r) as
�nr
�=
n!
r!(n� r)!=
n � (n� 1) � (n� 1) � : : : � (n� r + 1)
1 � 2 � 3 � : : : � r :
Note:
Most counting problems consist of drawing a �xed number of times from a set of elements (e.g.,
f1; 2; 3; 4; 5; 6g). To solve such problems, we need to know
(i) the size of the set, n;
(ii) the size of the sample, r;
(iii) whether the result will be ordered (i.e., is f1; 2g di�erent from f2; 1g); and
(iv) whether the draws are with replacement (i.e, can results like f1; 1g occur?).
Theorem 1.3.5:
The number of ways to draw r elements from a set of n, if
(i) ordered, without replacement, is n!(n�r)! ;
(ii) ordered, with replacement, is nr;
(iii) unordered, without replacement, is n!r!(n�r)! =
�nr
�;
(iv) unordered, with replacement, is(n+r�1)!r!(n�1)! =
�n+r�1r
�.
Proof:
(i) n choices to select 1st
n� 1 choices to select 2nd...
n� r + 1 choices to select rth
By Theorem 1.3.2, there are n�(n�1)� : : :�(n�r+1) =n�(n�1)�:::�(n�r+1)�(n�r)!
(n�r)! = n!(n�r)!
ways to do so.
12
Corollary:
The number of permutations of n objects is n!.
(ii) n choices to select 1st
n choices to select 2nd...
n choices to select rth
By Theorem 1.3.2, there are n� n� : : :� n| {z }r times
= nr ways to do so.
(iii) We know from (i) above that there are n!(n�r)! ways to draw r elements out of n elements
without replacement in the ordered case. However, for each unordered set of size r, there are
r! related ordered sets that consist of the same elements. Thus, there are n!(n�r)! �
1r! =
�nr
�ways to draw r elements out of n elements without replacement in the unordered case.
(iv) There is no immediate direct way to show this part. We have to come up with some extra
motivation. We assume that there are (n � 1) walls that separate the n bins of possible
outcomes and there are r markers. If we shake everything, there are (n�1+r)! permutations
to arrange these (n� 1) walls and r markers according to the Corollary. Since the r markers
are indistinguishable and the (n � 1) markers are also indistinguishable, we have to divide
the number of permutations by r! to get rid of identical permutations where only the markers
are changed and by (n � 1)! to get rid of identical permutations where only the walls are
changed. Thus, there are(n�1+r)!r!(n�1)! =
�n+r�1r
�ways to draw r elements out of n elements with
replacement in the unordered case.
Theorem 1.3.6: The Binomial Theorem
If n is a non{negative integer, then
(1 + x)n =nX
r=0
n
r
!xr
Proof: (By Induction)
Induction Base:
n = 0: 1 = (1 + x)0 =0X
r=0
0
r
!xr =
0
0
!x0 = 1
n = 1: (1 + x)1 =1X
r=0
1
r
!xr =
1
0
!x0 +
1
1
!x1 = 1 + x
13
Induction Step:
Suppose it is true for k. We show that it is true for k + 1.
(1 + x)k+1 = (1 + x)k(1 + x)
IB=
kX
r=0
k
r
!xr
!(1 + x)
=kX
r=0
k
r
!xr +
kXr=0
k
r
!xr+1
=
k
0
!x0 +
kXr=1
" k
r
!+
k
r � 1
!#xr +
k
k
!xk+1
=
k + 1
0
!x0 +
kXr=1
" k
r
!+
k
r � 1
!#xr +
k + 1
k + 1
!xk+1
(�)=
k + 1
0
!x0 +
kXr=1
k + 1
r
!xr +
k + 1
k + 1
!xk+1
=k+1Xr=0
k + 1
r
!xr
(*) Here we use Theorem 1.3.8 (i). Since the proof of Theorem 1.3.8 (i) only needs algebraic trans-
formations without using the Binomial Theorem, part (i) of Theorem 1.3.8 can be applied here.
Corollary 1.3.7:
For a non{negative integer n, it holds:
(i)�n0�+�n1�+ : : :+
�nn
�= 2n
(ii)�n0���n1�+�n2���n3�+ : : :+ (�1)n
�nn
�= 0
(iii) 1 ��n1�+ 2 �
�n2�+ 3 �
�n3�+ : : :+ n �
�nn
�= n2n�1
(iv) 1 ��n1�� 2 �
�n2�+ 3 �
�n3�+ : : :+ (�1)n�1n �
�nn
�= 0
Proof:
Use the Binomial Theorem:
(i) Let x = 1. Then
2n = (1 + 1)nBin:Th:=
nXr=0
n
r
!1r =
nXr=0
n
r
!
14
(ii) Let x = �1. Then
0 = (1 + (�1))n Bin:Th:=
nXr=0
n
r
!(�1)r
(iii) ddx(1 + x)n = d
dx
nXr=0
n
r
!xr
=) n(1 + x)n�1 =nX
r=1
r � n
r
!xr�1
Substitute x = 1, then
n2n�1 = n(1 + 1)n�1 =nX
r=1
r � n
r
!
(iv) Substitute x = �1 in (iii) above, then
0 = n(1 + (�1))n�1 =nXr=1
r � n
r
!(�1)r =
nXr=1
r � n
r
!(�1)r�1
since forPai = 0 also
P(�ai) = 0.
Note:
A useful extension for the binomial coe�cient for n < r is
�nr
�=
n � (n� 1) � : : : � 0 � : : : � (n� r + 1)
1 � 2 � : : : � r = 0:
Theorem 1.3.8:
For non{negative integers, n;m; r, it holds:
(i)
�n�1r
�+
�n�1r � 1
�=�nr
�
(ii)�n0��
mr
�+�n1�� m
r � 1
�+ : : :+
�nr
��m0�=
�m+nr
�
(iii)
�0r
�+
�1r
�+
�2r
�+ : : :+
�nr
�=
�n+1r + 1
�
Proof:
Homework
15
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 7: Wednesday 9/15/1999
Scribe: J�urgen Symanzik, Bill Morphet
1.4 Conditional Probability and Independence
So far, we have computed probability based only on the information that is used for a probabil-
ity space (; L; P ). Suppose, instead, we know that event H 2 L has happened. What statement
should we then make about the chance of an event A 2 L ?
De�nition 1.4.1:
Given (; L; P ) and H 2 L;P (H) > 0, and A 2 L, we de�ne
P (AjH) =P (A \H)
P (H)= PH(A)
and call this the conditional probability of A given H.
Note:
This is unde�ned if P (H) = 0.
Theorem 1.4.2:
In the situation of De�nition 1.4.1, (; L; PH ) is a probability space.
Proof:
If PH is a probability measure, it must satisfy Def. 1.1.7.
(i) P (H) > 0 and by Def. 1.1.7 (i) P (A \H) � 0 =) PH(A) =P (A\H)P (H) � 0 8A 2 L
(ii) PH() =P (\H)P (H) =
P (H)P (H) = 1
(iii) Let fAng1n=1 be a sequence of disjoint sets. Then,
PH(1[n=1
An)Def:1:4:1
=
P ((1[n=1
An) \H)
P (H)
=
P (1[n=1
(An \H))
P (H)
16
Def:1:1:7(iii)=
1Xn=1
P (An \H)
P (H)
=1Xn=1
(P (An \H)
P (H))
Def1:4:1=
1Xn=1
PH(An)
Note:
What we have done is to move to a new sample space H and a new �{�eld LH = L\H of subsets
A \ H for A 2 L. We thus have a new measurable space (H; LH) and a new probability space
(H; LH ; PH).
Note:
From De�nition 1.4.1, if A;B 2 L;P (A) > 0, and P (B) > 0, then
P (A \B) = P (A)P (BjA) = P (B)P (AjB);
which generalizes to
Theorem 1.4.3: Multiplication Rule
If A1; : : : ; An 2 L and P (n�1\j=1
Aj) > 0, then
P (n\j=1
Aj) = P (A1) � P (A2jA1) � P (A3jA1 \A2) � : : : � P (Anjn�1\j=1
Aj):
Proof:
Homework
De�nition 1.4.4:
A collection of subsets fAng1n=1 of form a partition of if
(i)1[n=1
An = , and
(ii) Ai \Aj = � 8i 6= j, i.e., elements are pairwise disjoint.
17
Theorem 1.4.5: Law of Total Probability
If fHjg1j=1 is a partition of , and P (Hj) > 0 8j, then, for A 2 L,
P (A) =1Xj=1
P (A \Hj) =1Xj=1
P (Hj)P (AjHj):
Proof:
By the Note preceding Theorem 1.4.3, the summands on both sides are equal
=) the right side of Th. 1.4.5 is true.
The left side proof:
Hj are disjoint ) A \Hj are disjoint
A = A \ Def1:4:4
= A \ (1[j=1
Hj) =1[j=1
(A \Hj)
=) P (A) = P (1[j=1
(A \Hj))Def1:1:7(iii)
=1Xj=1
P (A \Hj)
Theorem 1.4.6: Bayes' Rule
Let fHjg1j=1 be a partition of , and P (Hj) > 0 8j. Let A 2 L and P (A) > 0. Then
P (HjjA) =P (Hj)P (AjHj)
1Xn=1
P (Hn)P (AjHn)
8j:
Proof:
P (Hj \A)Def1:4:1
= P (A) � P (HjjA) = P (Hj) � P (AjHj)
=) P (HjjA) = P (Hj)�P (AjHj)P (A)
Th:1:4:5=
P (Hj)�P (AjHj)1Xn=1
P (Hn)P (AjHn)
.
De�nition 1.4.7:
For A;B 2 L, A and B are independent i� P (A \B) = P (A)P (B).
Note:
� There are no restrictions on P (A) or P (B).
� If A and B are independent, then P (AjB) = P (A) (given that P (B) > 0) and P (BjA) = P (B)
(given that P (A) > 0).
18
� If A and B are independent, then the following events are independent as well: A and BC ;
AC and B; AC and B
C .
De�nition 1.4.8:
Let A be a collection of L{sets. The events of A are pairwise independent i� for every distinct
A1; A2 2 A it holds P (A1 \A2) = P (A1)P (A2).
De�nition 1.4.9:
Let A be a collection of L{sets. The events of A are mutually independent (or completely inde-
pendent) i� for every �nite subcollection fAi1 ; : : : ; Aikg; Aij 2 A, it holds P (k\
j=1
Aij ) =kY
j=1
P (Aij ).
Note:
To check for mutually independence of n events fA1; : : : ; Ang 2 L, there are 2n � n � 1 relations
(i.e., all subcollections of size 2 or more) to check.
Example 1.4.10:
Flip a fair coin twice. = fHH;HT; TH; TTg.A1 = \H on 1st toss"
A2 = \H on 2nd toss"
A3 = \Exactly one H"
Obviously, P (A1) = P (A2) = P (A3) =12 .
Question: Are A1; A2 and A3 pairwise independent and also mutually independent?
P (A1 \A2) = :25 = :5 � :5 = P (A1) � P (A2)) A1; A2 are independent.
P (A1 \A3) = :25 = :5 � :5 = P (A1) � P (A3)) A1; A3 are independent.
P (A2 \A3) = :25 = :5 � :5 = P (A2) � P (A3)) A2; A3 are independent.
Thus, A1; A2; A3 are pairwise independent.
P (A1\A2\A3) = 0 6= :5�:5�:5 = P (A1)�P (A2)�P (A3)) A1; A2; A3 are not mutually independent.
19
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 8: Friday 9/17/1999
Scribe: J�urgen Symanzik, Rich Madsen
Example 1.4.11: (from Rohatgi, page, Example 5)
� r students. 365 possible birthdays for each student that are equally likely.
� One student at a time is asked for his/her birthday.
� If one of the other students hears this birthday and it matches his/her birthday, this other
student has to raise his/her hand | if at least one other student raises his/her hand, the
procedure is over.
� We are interested in
pk = P (procedure terminates at the kth student)
= P (a hand is �rst risen when the kth student is asked for his/her birthday)
It is
p1 = P (at least 1 other (from r-1) students has a birthday on this particular day.)
= 1� P (all (r-1) students have a birthday on the remaining 364 out of 365 days)
= 1��364
365
�r�1
p2 = P (no student has a birthday matching the �rst student and at least one
of the other (r-2) students has a b-day matching the second student)
Let A � No student has a b-day matching the 1ststudent
Let B � At least one of the other (r-2) has b-day matching 2nd
So p2 = P (A \B)
= P (A) � P (BjA)
= P (no student has a matching b-day with the 1ststudent )�
P (at least one of the remaining students has a mathching b-day with the second,
given that no one matched the �rst.)
20
= (1� p1)[1 � P (all (r-2) students have a b-day on the remaining 363 out of 364 days)
=
�364
365
�r�1 "1�
�363
364
�r�2#
=
�365 � 1
365
�r�1 "1�
�363
364
�r�2#
Working backwards from the book
p2 =
�365P2�1(365)2�1
��1� 2� 1
365
�r�2+1"1�
�365 � 2
365 � 2 + 1
�r�2#
=365
365
�1� 1
365
�r�1 "1�
�363
364
�r�2#
=
�365� 1
365
�r�1 "1�
�363
364
�r�2#
(Same as what we found before)
p3 = P (No one has same b-day as �rst and no one same as second, and at least one of the
remaining (r � 3) has a matching b-day with the 3rd student)
Let A � No one has the same b-day as the �rst student
Let B � No one has the same b-day as the second student
Let C � At least one of the other (r � 3) has the same b-day as the third students
Now:
p3 = P (A \B \ C)
= P (A) � P (BjA) � P (CjA \B)
=
�364
365
�r�1 �363364
�r�2� [1� P (all (r � 3) students have a b-day on the remaining 362 out of 363 days]
=
�364
365
�r�1 �363364
�r�2�"1�
�362
363
�r�3#
=(364)r�1
(365)r�1� (363)
r�2
(364)r�2�"1�
�362
363
�r�3#
=
364r�1
364r�2
! 363r�2
365r�1
!�"1�
�362
363
�r�3#
21
=
�364
365
� 363r�2
365r�2
!�"1�
�362
363
�r�3#
Working backwards from the book
p3 =
�365P3�1(365)3�1
��1� 3� 1
365
�r�3+1"1�
�365 � 3
365 � 3 + 1
�r�3#
=
�365P2
(365)2
��1� 2
365
�r�2 "1�
�362
363
�r�3#
=
�(365)(364)
(365)2
��363
365
�r�2 "1�
�362
363
�r�3#
=
�364
365
��363
365
�r�2 "1�
�362
363
�r�3#
(Same as what we found before)
For general pk and restrictions on r and k see Homework.
22
2 Random Variables
2.1 Measurable Functions
De�nition 2.1.1:
� A random variable (rv) is a set function from to IR.
� More formally: Let (; L; P ) be any probability space. Suppose X : ! IR and that X is a
measurable function, then we call X a random variable.
� More generally: IfX : ! IRk, we callX a random vector, X = (X1(!); X2(!); : : : ;Xk(!)).
What does it mean to say that a function is measurable?
De�nition 2.1.2:
Suppose (; L) and (S;B) are two measurable spaces and X : ! S is a mapping from to S. We
say that X is measurable L � B if X�1(B) 2 L for every set B 2 B, where X�1(B) = f! 2 :
X(!) 2 Bg.
Example 2.1.3:
Record the opinion of 50 people: \yes" (y) or \no" (n).
= fAll 250 possible sequences of y/ng | HUGE !
L = P()
X : ! S = fAll 250 possible sequences of 1 (=y) and 0 (=n)gB = P(S)
X is a random vector since each element in S has a corresponding element in , for B 2 B;X�1(B) 2
L = P().
Consider X : ! S = f0; 1; 2; : : : ; 50g, where X(!) = \# of y's in !" is a more manageable
random variable.
A simple function, which takes only �nite many values x1; : : : ; xk is measurable i�
X�1(xi) 2 L 8xi.
Here, X�1(k) = f! 2 : # 1's in sequence ! = kg is a subset of , so it is in L = P()
23
Example 2.1.4:
Let = \in�nite fair coin tossing space", i.e., in�nite sequence of H's and T's.
Let Ln be a �{�eld for the 1st n tosses.
De�ne L = �(1[n=1
Ln).
Let Xn : ! IR be Xn(!) = \proportion of H's in 1st n tosses".
For each n, Xn(�) is simple (values f0; 1n ;2n ; : : : ; ng) and X
�1n ( kn) 2 Ln 8k = 0; 1; : : : ; n.
Therefore, X�1n ( kn) 2 L.
So every random variable Xn(�) is measurable L�B. Now we have a sequence of rv's fXng1n=1. We
will show later that P (f! : Xn(!)! 12g) = 1, i.e., the Strong Law of Large Numbers (SLLN).
24
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 9: Monday 9/20/1999
Scribe: J�urgen Symanzik
Some Technical Points about Measurable Functions
2.1.5:
Suppose (; L) and (S;B) are measure spaces and that a collection of sets A generates B, i.e.,
�(A) = B. Let X : ! S. If X�1(A) 2 L 8A 2 A, then X is measurable L� B.
This means we only have to check measurability on a basis collection A. The usage is: B on IR is
generated by f(�1; x] : x 2 IRg.
2.1.6:
If (; L); (0; L0), and (00; L00) are measure spaces and X : ! 0 and Y : 0 ! 00 are measur-
able, then the composition (Y X) : ! 00 is measurable L� L00.
2.1.7:
If f : IRi ! IRk is a continuous function, then f is measurable Bi � Bk.
2.1.8:
If fj : ! IR; j = 1; : : : k and g : IRk ! IR are measurable, then g(f1(�); : : : ; fk(�)) is measurable.
The usage is: g could be sum, average, di�erence, product, (�nite) maximums and minimums of
x1; : : : ; xk, etc.
2.1.9:
Limits: Extend the real line to [�1;1] = IR [ f�1;1g.We say f : ! IR is measurable L� B if
(i) f�1(B) 2 L 8B 2 B, and
(ii) f�1(�1); f�1(1) 2 L also.
25
2.1.10:
Suppose f1; f2; : : : is a sequence of real{valued measurable functions (; L) ! (IR;B). Then it
holds:
(i) supnfn; inf
nfn; lim sup
nfn; lim inf
nfn, are measurable.
(ii) If f = limnfn exists, then f is measurable.
(iii) The set f! : fn(!) convergesg 2 L.
(iv) If f is any measurable function, the set f! : fn(!)! f(!)g 2 L.
26
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 10: Wednesday 9/22/1999
Scribe: J�urgen Symanzik, Bill Morphet
2.2 Probability Distribution of a Random Variable
The de�nition of a random variable X : (; L)! (S;B) makes no mention of P . We now introduce
a probability measure on (S;B).
Theorem 2.2.1:
A random variable X on (; L; P ) induces a probability measure on a space (IR;B; Q) with the
probability distribution Q of X de�ned by
Q(B) = P (X�1(B)) = P (f! : X(!) 2 Bg) 8B 2 B:
Note:
By the de�nition of a random variable, X�1(B) 2 L 8B 2 B.
Proof:
If X induces a probability measure Q on (IR;B), then Q must satisfy the Kolmogorov Axioms of
probability.
X : (; L)! (S;B). X is a rv ) X�1(B) = f! : X(!) 2 Bg = A 2 L 8B 2 B.
(i) Q(B) = P (X�1(B)) = P (f! : X(!) 2 Bg) = P (A)Def:1:1:7(i)
� 0 8B 2 B
(ii) Q(IR) = P (X�1(IR)) X rv= P ()
Def:1:1:7(ii)= 1
(iii) Let fBng1n=1 2 B; Bi \Bj = ; 8i 6= j. Then,
Q(1[n=1
Bn) = P (X�1(1[n=1
Bn))(�)= P (
1[n=1
(X�1(Bn)))Def:1:1:7(iii)
=1Xn=1
P (X�1(Bn)) =1Xn=1
Q(Bn)
(�) holds since X�1(�) commutes with unions/intersections and preserves disjointedness.
27
De�nition 2.2.2:
A real{valued function F on (�1;1) that is non{decreasing, right{continuous, and satis�es
F (�1) = 0; F (1) = 1
is called a cumulative distribution function (cdf) on IR.
Note:
No mention of probability space or measure P in De�nition 2.2.2 above.
De�nition 2.2.3:
Let P be a probability measure on (IR;B). The cdf associated with P is
F (x) = FP (x) = P ((�1; x]) = P (f! : X(!) � xg) = P (X � x)
for a random variable X de�ned on (IR;B; P ).
Note:
F (�) de�ned as in De�nition 2.2.3 above indeed is a cdf.
Proof:
(i) Let x1 < x2
=) (�1; x1] � (�1; x2]
=) F (x1) = P (f! : X(!) � x1g)Th:1:2:1(v)
� P (f! : X(!) � x2g) = F (x2)
Thus, since x1 < x2 and F (x1) � F (x2), F (:) is non{decreasing.
(ii) Since F is non-decreasing, it is su�cient to show that F (:) is right{continuous if for any
sequence of numbers xn ! x+ (which means that xn is approaching x from the right) with
x1 > x2 > : : : > xn > : : : > x : F (Xn)! F (X):
Let An = f! : X(!) 2 (x; xn]g 2 L and An # ;. None of the intervals (x; xn] contains x. Asxn ! x+, the number of points ! in An diminishes until the set is empty. Formally,
limn!1An = lim
n!1
n\i=1
Ai =1\n=1
An = ;.
By Theorem 1.2.6 it follows that
limn!1P (An) = P ( lim
n!1An) = P (;) = 0.
28
It is
P (An) = P (f! : X(!) � xng)� P (f! : X(!) � xg) = F (xn)� F (x).
=) ( limn!1F (xn))� F (x) = lim
n!1(F (xn)� F (x)) = limn!1P (An) = 0
=) limn!1F (xn) = F (x)
=) F (x) is right{continuous.
(iii) F (�n) Def: 2:2:3= P (f! : X(!) � �ng)
=)F (�1) = lim
n!1F (�n)
= limn!1P (f! : X(!) � �ng)
= P ( limn!1f! : X(!) � �ng)
= P (;)
= 0
(iv) F (n)Def: 2:2:3
= P (f! : X(!) � ng)=)
F (1) = limn!1F (n)
= limn!1P (f! : X(!) � ng)
= P ( limn!1f! : X(!) � ng)
= P ()
= 1
Note that (iii) and (iv) implicitly use Theorem 1.2.6. In (iii), we use An = (�1;�n) where
An � An+1 and An # ;. In (iv), we use An = (�1; n) where An � An+1 and An " IR.
De�nition 2.2.4:
If a random variable X : ! IR has induced a probability measure PX on (IR;B) with cdf F (x),
we say
(i) rv X is continuous if F (x) is continuous in x.
(ii) rv X is discrete if F (x) is a step function in x.
29
Note:
There are rvs that are mixtures of continuous and discrete rvs. One such example is a truncated
failure time distribution. We assume a continuous distribution (e.g., exponential) up to a given
truncation point x and assign the \remaining" probability to the truncation point. Thus, a single
point has a probability > 0 and F (x) jumps at the truncation point x.
De�nition 2.2.5:
Two random variablesX and Y are identically distributed i� PX(X 2 A) = PY (Y 2 A) 8A 2 L.
Note:
Def. 2.2.5 does not mean that X(!) = Y (!) 8! 2 . For example,
X = # H in 3 coin tosses
Y = # T in 3 coin tosses
X;Y are both Bin(3; 0:5), i.e., identically distributed, but for ! = (H;H; T );X(!) = 2 6= 1 = Y (!),
i.e., X 6= Y .
Theorem 2.2.6:
The following two statements are equivalent:
(i) X;Y are identically distributed.
(ii) FX(x) = FY (x) 8x 2 IR.
Proof:
(i) ) (ii):
FX(x) = PX((�1; x])
= P (f! : X(!) 2 (�1; x]g)byDef:2:2:5
= P (f! : Y (!) 2 (�1; x]g)
= PY ((�1; x])
= FY (X)
(ii) ) (i):
Requires extra knowledge from measure theory.
30
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 11: Friday 9/24/1999
Scribe: J�urgen Symanzik
2.3 Discrete and Continuous Random Variables
We now extend De�nition 2.2.4 to make our de�nitions a little bit more formal.
De�nition 2.3.1:
Let X be a real{valued random variable with cdf F on (; L; P ). X is discrete if there exists a
countable set E � IR such that P (X 2 E) = 1, i.e., P (f! : X(!) 2 Eg) = 1. The points of E
which have positive probability are the jump points of the step function F , i.e., the cdf of X.
De�ne pi = P (f! : X(!) = xi; xi 2 Eg) = PX(X = xi) 8i � 1. Then, pi � 0;1Xi=1
pi = 1.
We call fpi : pi � 0g the probability mass function (pmf) (also: probability frequency function)
of X.
Note:
Given any set of numbers fpng1n=1; pn � 0 8n � 1;1Xn=1
pn = 1, fpng1n=1 is the pmf of some rv X.
Note:
The issue of continuous rv's and probability density functions (pdfs) is more complicated. A rv
X : ! IR always has a cdf F . Whether there exists a function f such that f integrates to F
and F 0 exists and equals f (almost everywhere) depends on something stronger than just continuity.
De�nition 2.3.2:
A real{valued function F is continuous in x0 2 IR i�
8� > 0 9� > 0 8x : j x� x0 j< � )j F (x)� F (x0) j< �:
F is continuous i� F is continuous in all x 2 R.
31
De�nition 2.3.3:
A real{valued function F de�ned on [a; b] is absolutely continuous on [a; b] i�
8� > 0 9� > 0 8 �nite subcollection of disjoint subintervals [ai; bi]; i = 1; : : : ; n :
nXi=1
(bi � ai) < � )nXi=1
j F (bi)� F (ai) j< �:
Note:
Absolute continuity implies continuity.
Theorem 2.3.4:
(i) If F is absolutely continuous, then F0 exists almost everywhere.
(ii) A function F is an inde�nite integral i� it is absolutely continuous. Thus, every absolutely
continuous function F is the inde�nite integral of its derivative F 0.
De�nition 2.3.5:
LetX be a random variable on (; L; P ) with cdf F . We sayX is a continuous rv i� F is absolutely
continuous. In this case, there exists a non{negative integrable function f , the probability density
function (pdf) of X, such that
F (x) =
Z x
�1f(t)dt = P (X � x):
From this it follows that, if a; b 2 IR; a < b, then
PX(a < X � b) = F (b)� F (a) =
Z b
af(t)dt
exists and is well de�ned.
Theorem 2.3.6:
Let X be a continuous random variable with pdf f . Then it holds:
(i) For every Borel set B 2 B; P (B) =ZBf(t)dt.
(ii) If F is absolutely continuous and f is continuous at x, then F0(x) = dF (x)
dx = f(x).
32
Proof:
Part (i): From De�nition 2.3.5 above.
Part (ii): By Fundamental Theorem of Calculus.
Note:
As already stated in the Note following De�nition 2.2.4, not every rv will fall into one of these two
(or if you prefer { three {, i.e., discrete, continuous/absolutely continuous) classes. However, most
rv which arise in practice will. We look at one example that is unlikely to occur in practice in the
next Homework assignment.
However, note that every cdf F can be written as
F (x) = aFd(x) + (1� a)Fc(x); 0 � a � 1;
where Fd is the cdf of a discrete rv and Fc is a continuous (but not necessarily absolute continuous)
cdf.
Some authors, such as Marek Fisz Wahrscheinlichkeitsrechnung und mathematische Statistik, VEB
Deutscher Verlag der Wissenschaften, Berlin, 1989, are even more speci�c. There it is stated that
every cdf F can be written as
F (x) = a1Fd(x) + a2Fc(x) + a3Fs(x); a1; a2; a3 � 0; a1 + a2 + a3 = 1:
Here, Fd(x) and Fc(x) are discrete and continuous cdfs (as above). Fs(x) is called a singular cdf.
Singular means that Fs(x) is continuous and its derivative F 0(x) equals 0 almost everywhere (i.e.,
everywhere but in those points that belong to a Borel{measurable set of probability 0).
Question: Does \continuous" but \not absolutely continuous" mean \singular"? | We will (hope-
fully) see later: : :
33
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 12: Monday 9/27/1999
Scribe: J�urgen Symanzik, Hanadi B. Eltahir
Example 2.3.7:
Consider
F (x) =
8>>>>><>>>>>:
0; x < 0
1=2; x = 0
1=2 + x=2; 0 < x < 1
1; x � 1
We can write F (x) as aFd(x) + (1� a)Fc(x); 0 � a � 1. How?
Since F (x) has only one jump at x = 0, it is reasonable to get started with a pmf p0 = 1 and
corresponding cdf
Fd(x) =
(0; x < 0
1; x � 0
Since F (x) = 0 for x < 0 and F (x) = 1 for x � 1, it must clearly hold that Fc(x) = 0 for x < 0
and Fc(x) = 1 for x � 1. In addition F (x) increases linearly in 0 < x < 1. A good guess would be
a pdf fc(x) = 1 � I(0;1)(x) and corresponding cdf
Fc(x) =
8>><>>:
0; x � 0
x; 0 < x < 1
1; x � 1
Knowing that F (0) = 1=2, we have at least to multiply Fd(x) by 1=2. And, indeed, F (x) can be
written as
F (x) =1
2Fd(x) +
1
2Fc(x):
De�nition 2.3.8:
The two{valued function IA(x) is called indicator function and it is de�ned as follows:
IA(x) = 1 if x 2 A and IA(x) = 0 if x 62 A for any set A.
34
An Excursion into Logic
When proving theorems we only used direct methods so far. We used induction proofs to show that
something holds for arbitrary n. To show that a statement A implies a statement B, i.e., A) B,
we used proofs of the type A ) A1 ) A2 ) : : : ) An�1 ) An ) B where one step directly
follows from the previous step. However, there are di�erent approaches to obtain the same result.
A) B is equivalent to :B ) :A is equivalent to :A _B:
A B A) B :A :B :B ) :A :A _B1 1 1 0 0 1 1
1 0 0 0 1 0 0
0 1 1 1 0 1 1
0 0 1 1 1 1 1
A, B is equivalent to (A) B) ^ (B ) A) is equivalent to (:A _B) ^ (A _ :B):
A B A, B A) B B ) A (A) B) ^ (B ) A) :A _B A _ :B (:A _B) ^ (A _ :B)1 1 1 1 1 1 1 1 1
1 0 0 0 1 0 0 1 0
0 1 0 1 0 0 1 0 0
0 0 1 1 1 1 1 1 1
Negations of Quanti�ers:
:8x 2 X : B(x) is equivalent to 9x 2 X : :B(x)
:9x 2 X : B(x) is equivalent to 8x 2 X : :B(x)
9x 2 X 8y 2 Y : B(x; y) implies 8y 2 Y 9x 2 X : B(x; y)
35
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 13: Wednesday 9/29/1999
Scribe: J�urgen Symanzik, Rich Madsen
2.4 Transformations of Random Variables
Let X be a real{valued random variable on (; L; P ), i.e., X : (; L) ! (IR;B). Let g be any
Borel{measurable real{valued function on IR. Then, by statement 2.1.6, Y = g(X) is a random
variable.
Theorem 2.4.1:
Given a random rariable X with known induced distribution and a Borel{measurable function g,
then the distribution of the random variable Y = g(X) is determined.
Proof:
FY (y) = PY (Y � y)
= P (f! : g(X(!)) � yg)
= P (f! : X(!) 2 Byg) where By = g�1(�1; y] 2 B since g is Borel-measureable.
= P (X�1(By))
Note:
From now on, we restrict ourselves to real{valued (vector{valued) functions that are Borel{measurable,
i.e., measurable with respect to (IR;B) or (IRk;Bk).
More generally, PY (Y 2 C) = PX(X 2 g�1(C)) 8C 2 B.
Example 2.4.2:
Suppose X is a discrete random variable. Let A be a countable set such that P (X 2 A) = 1 and
P (X = x) > 0 8x 2 A.
Let Y = g(X). Obviously, the sample space of Y is also countable. Then,
PY (Y = y) =X
x2g�1(fyg)PX(X = x) =
Xfx:g(x)=yg
PX(X = x) 8y 2 g(A)
36
Example 2.4.3:
X � U(�1; 1) so the pdf of X is fX(x) = 1=2I[�1;1](x), which, according to De�nition 2.3.8, reads
as fX(x) = 1=2 for �1 � x � 1 and 0 otherwise.
Let Y = X+ =
(x; x � 0
0; otherwise
Then,
FY (y) = PY (Y � y) =
8>>>>><>>>>>:
0; y < 0
1=2; y = 0
1=2 + y=2; 0 < y < 1
1; y � 1
This is the mixed discrete/continuous distribution from Example 2.3.7.
Note:
We need to put some conditions on g to ensure g(X) is continuous if X is continuous and avoid
cases as in Example 2.4.3 above.
De�nition 2.4.4:
For a random variable X from (; L; P ) to (IR;B), the support of X (or P ) is any set A 2 L for
which P (A) = 1. For a continuous random variable X with pdf f , we can think of the support of
X as X = X�1(fx : fX(x) > 0g).
De�nition 2.4.5:
Let f be a real{valued function de�ned on D � IR;D 2 B. We say:
f is (strictly) non{decreasing if x < y ) f(x) (<) � f(y) 8x; y 2 D
f is (strictly) non{increasing if x < y ) f(x) (>) � f(y) 8x; y 2 D
f is monotonic on D if f is either increasing or decreasing and write f " or f #.
Theorem 2.4.6:
Let X be a continuous rv with pdf fX and support X. Let y = g(x) be di�erentiable for all x and
either (i) g0(x) > 0 or (ii) g0(x) < 0 for all x.
Then, Y = g(X) is also a continuous rv with pdf
fY (y) = fX(g�1(y))� j d
dyg�1(y) j �Ig(X)(y):
37
Proof:
Part (i): g0(x) > 0 8x 2 X
So g is strictly increasing and continuous.
Therefore, x = g�1(y) exists and it is also strictly increasing and also di�erentiable.
Then, from Rohatgi, page 9, Theorem 15:
d
dyg�1(y) =
�d
dxg(x) jx=g�1(y)
��1> 0
We get FY (y) = PY (Y � y) = PY (g(X) � y) = PX(X � g�1(y)) = FX(g
�1(y)) for y 2 g(X) and,
by di�erentiation,
fY (y) = F0Y (y) =
d
dy(FX(g
�1(y)))By Chain Rule
= fX(g�1(y)) � d
dyg�1(y)
Part (ii): g0(x) < 0 8x 2 X
So g is strictly decreasing and continuous.
Therefore, x = g�1(y) exists and it is also strictly decreasing and also di�erentiable.
Then, from Rohatgi, page 9, Theorem 15:
d
dyg�1(y) =
�d
dxg(x) jx=g�1(y)
��1< 0
We get FY (y) = PY (Y � y) = PY (g(X) � y) = PX(X � g�1(y)) = 1 � PX(X � g
�1(y)) =
1� FX(g�1(y)) for y 2 g(X) and, by di�erentiation,
fY (y) = F0Y (y) =
d
dy(1�FX(g�1(y)))
By Chain Rule= �fX(g�1(y))�
d
dyg�1(y) = fX(g
�1(y))��� d
dyg�1(y)
�
Since ddy g
�1(y) < 0, the negative sign will cancel out, always giving us a positive value. Hence the
need for the absolute value signs.
Combining parts (i) and (ii), we can therefore write
fY (y) = fX(g�1(y))� j d
dyg�1(y) j �Ig(X)(y):
38
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lectures 14 & 15: Friday 10/1/1999 & Monday 10/4/1999
Scribe: J�urgen Symanzik, Hanadi B. Eltahir
Note:
In Theorem 2.4.6, we can also write
fY (y) =f(x)
j dg(x)dx j
�����x=g�1(y)
; y 2 g(X)
If g is monotonic over disjoint intervals, we can also get an expression for the pdf/cdf of Y = g(X)
as stated in the following theorem:
Theorem 2.4.7:
Let Y = g(X) where X is a rv with pdf fX(x) on support X. Suppose there exists a partition
A0; A1; : : : ; Ak of X such that P (X 2 A0) = 0 and fX(x) is continuous on each Ai. Suppose there
exist functions g1(x); : : : ; gk(x) de�ned on A1 through Ak, respectively, satisfying
(i) g(x) = gi(x) 8x 2 Ai,
(ii) gi(x) is monotonic on Ai,
(iii) the set Y = gi(Ai) = fy : y = gi(x) for some x 2 Aig is the same for each i = 1; : : : ; k, and
(iv) g�1i (y) has a continuous derivative on Y for each i = 1; : : : ; k.
Then,
fY (y) =kXi=1
fX(g�1i (y))� j d
dyg�1i (y) j �IY(y)
Note:
Rohatgi, page 73, Theorem 4, removes condition (iii) by de�ning n = n(y) and x1(y); : : : ; xn(y).
39
Example 2.4.8:
Let X be a rv with pdf fX(x) =2x�2� I(0;�)(x).
Let Y = sin(X). What is fY (y)?
Since sin is not monotonic on (0; �), Theorem 2.4.6 cannot be used to determine the pdf of Y .
Two possible approaches:
Method 1: cdfs
For 0 < y < 1 we have
FY (y) = PY (Y � y)
= PX(sinX � y)
= PX([0 � X � sin�1(y)] or [� � sin�1(y) � X � �])
= FX(sin�1(y)) + (1� FX(� � sin�1(y)))
since [0 � X � sin�1(y)] and [� � sin�1(y) � X � �] are disjoint sets. Then,
fY (y) = F0Y (y)
= fX(sin�1(y))
1p1� y2
+ (�1)fX(� � sin�1(y))�1p1� y2
=1p
1� y2
�fX(sin
�1(y)) + fX(� � sin�1(y))�
=1p
1� y2
2(sin�1(y))
�2+2(� � sin�1(y))
�2
!
=1
�2p1� y2
2�
=2
�p1� y2
� I(0;1)(y)
Method 2: Use of Theorem 2.4.7
Let A1 = (0; �2 ), A2 = (�2 ; �), and A0 = f�2 g.
Let g�11 (y) = sin�1(y) and g�12 (y) = � � sin�1(y).
It is ddyg�11 (y) = 1p
1�y2= � d
dyg�12 (y) and Y = (0; 1).
Thus, by use of Theorem 2.4.7, we get
fY (y) =2Xi=1
fX(g�1i (y))� j d
dyg�1i (y) j �IY(y)
40
=2 sin�1(y)
�2
1p1� y2
� I(0;1)(y) +2(� � sin�1(y))
�2
1p1� y2
� I(0;1)(y)
=2�
�2
1p1� y2
� I(0;1)(y)
=2
�p1� y2
� I(0;1)(y)
Obviously, both results are identical.
Theorem 2.4.9:
Let X be a rv with a continuous cdf FX(x) and let Y = FX(X). Then, Y � U(0; 1).
Proof:
We have to consider two possible cases:
(a) FX is strictly increasing, i.e., FX(x1) < FX(x2) for x1 < x2, and
(b) FX is non{decreasing, i.e., there exists x1 < x2 and FX(x1) = FX(x2). Assume that x1 is
the in�mum and x2 the supremum of those values for which FX(x1) = FX(x2) holds.
In (a), F�1X (y) is uniquely de�ned. In (b), we de�ne F�1
X (y) = inffx : FX(x) � yg
Without loss of generality:
F�1X (1) = +1 if FX(x) < 1 8x 2 IR and
F�1X (0) = �1 if FX(x) > 0 8x 2 IR.
For Y = FX(X) and 0 < y < 1, we have
P (Y � y) = P (FX(X) � y)
F�1X
"= P (F�1
X (FX(X)) � F�1X (y))
(�)= P (X � F
�1X (y))
= FX(F�1X (y))
= y
At the endpoints, we have P (Y � y) = 1 if y � 1 and P (Y � y) = 0 if y � 0.
But why is (�) true? | In (a), if FX is strictly increasing and continuous, it is certainly x =
F�1X (FX (x)).
In (b), if FX(x1) = FX(x2) for x1 < x < x2, it may be that F�1X (FX (x)) 6= x. But by de�nition,
F�1X (FX (x)) = x1 8x 2 [x1; x2]. (�) holds since on [x1; x2], it is P (X � x) = P (X � x1) 8x 2
[x1; x2]. The at cdf denotes FX(x2)� FX(x1) = P (x1 < X � x2) = 0 by de�nition.
41
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 16: Wednesday 10/6/1999
Scribe: J�urgen Symanzik
3 Moments and Generating Functions
3.1 Expectation
De�nition 3.1.1:
Let X be a real-valued rv with cdf FX and pdf fX if X is continuous (or pmf fX and support X if
X is discrete). The expected value (mean) of a measurable function g(�) of X is
E(g(X)) =
8>>>><>>>>:
Z 1
�1g(x)fX(x)dx; if X is continuous
Xx2X
g(x)fX(x); if X is discrete
if E(j g(X) j) <1; otherwise E(g(X)) is unde�ned, i.e., it does not exist.
Example:
X � Cauchy; fX(x) =1
�(1+x2);�1 < x <1:
E(j X j) = 2�
Z 1
0
x
1 + x2dx =
1
�[log(1 + x
2)]10 =1
So, E(X) does not exist for the Cauchy distribution.
Theorem 3.1.2:
If E(X) exists and a and b are �nite constants, then E(aX + b) exists and equals aE(X) + b.
Proof:
Continuous case only:
Existence:
E(j aX + b j) =
Z 1
�1j ax+ b j fX(x)dx
�Z 1
�1(j a j � j x j + j b j)fX(x)dx
= j a jZ 1
�1j x j fX(x)dx+ j b j
Z 1
�1fX(x)dx
42
= j a j E(j X j)+ j b j
< 1
Numerical Result:
E(aX + b) =
Z 1
�1(ax+ b)fX(x)dx
= a
Z 1
�1xfX(x)dx+ b
Z 1
�1fX(x)dx
= aE(X) + b
Theorem 3.1.3:
If X is bounded (i.e., there exists a M; 0 < M < 1, such that P (j X j< M) = 1), then E(X)
exists.
De�nition 3.1.4:
The kth moment of X, if it exists, is mk = E(Xk).
The kth central moment of X, if it exists, is �k = E((X �E(X))k).
De�nition 3.1.5:
The variance of X, if it exists, is the second central moment of X, i.e.,
V ar(X) = E((X �E(X))2):
Theorem 3.1.6:
V ar(X) = E(X2)� (E(X))2.
Proof:
V ar(X) = E((X �E(X))2)
= E(X2 � 2XE(X) + (E(X))2)
= E(X2)� 2E(X)E(X) + (E(X))2
= E(X2)� (E(X))2
43
Theorem 3.1.7:
If V ar(X) exists and a and b are �nite constants, then V ar(aX + b) exists and equals a2V ar(X).
Proof:
Existence & Numerical Result:
V ar(aX + b) = E�((aX + b)�E(aX + b))2
�exists if E
�j ((aX + b)�E(aX + b))2 j
�exists.
It holds that
E
�j ((aX + b)�E(aX + b))2 j
�= E
�((aX + b)�E(aX + b))2
�= V ar(aX + b)
Th:3:1:6= E((aX + b)2)� (E(aX + b))2
Th:3:1:2= E(a2X2 + 2abX + b
2)� (aE(X) + b)2
Th:3:1:2= a
2E(X2) + 2abE(X) + b
2 � a2(E(X))2 � 2abE(X) � b
2
= a2(E(X2)� (E(X))2)
Th:3:1:6= a
2V ar(X)
< 1 since V ar(X) exists
44
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 17: Friday 10/8/1999
Scribe: J�urgen Symanzik
Theorem 3.1.8:
If the tth moment of a rv X exists, then all moments of order 0 < s < t exist.
Proof:
Continuous case only:
E(j X js) =
Zjxj�1
j x js fX(x)dx+Zjxj>1
j x js fX(x)dx
�Zjxj�1
1 � fX(x)dx+Zjxj>1
j x jt fX(x)dx
� P (j X j� 1) +E(j X jt)
< 1
Theorem 3.1.9:
If the tth moment of a rv X exists, then
limn!1n
tP (j X j> n) = 0:
Proof:
Continuous case only:
1 >
ZIRj x jt fX(x)dx = lim
n!1
Zjxj�n
j x jt fX(x)dx
=) limn!1
Zjxj>n
j x jt fX(x)dx = 0
But, limn!1
Zjxj>n
j x jt fX(x)dx � limn!1n
tZjxj>n
fX(x)dx = limn!1n
tP (j X j> n) = 0
Note:
The inverse is not necessarily true, i.e., if limn!1n
tP (j X j> n) = 0, then the tth moment of a rv
X does not necessarily exist. We can only approach t up to some � > 0 as the following Theorem
3.1.10 indicates.
45
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 18: Monday 10/11/1999
Scribe: J�urgen Symanzik, Bill Morphet
Theorem 3.1.10:
Let X be a rv with a distribution such that limn!1n
tP (j X j> n) = 0 for some t > 0. Then,
E(j X js) <1 8 0 < s < t:
Note:
To prove this Theorem, we need Lemma 3.1.11 and Corollary 3.1.12.
Lemma 3.1.11:
Let X be a non{negative rv with cdf F . Then,
E(X) =
Z 1
0(1� FX(x))dx
(if either side exists).
Proof:
Continuous case only:
To prove that the left side implies that the right side is �nite and both sides are identical, we
assume that E(X) exists. It is
E(X) =
Z 1
0xfX(x)dx = lim
n!1
Z n
0xfX(x)dx
Replace the expression for the right side integral using integration by parts.
Let u = x and dv = fX(x)dx, then
Z n
0xfX(x)dx = (xF (x)) jn0 �
Z n
0FX(x)dx
= nFX(n)� 0FX (0)�Z n
0FX(x)dx
= nFX(n)� n+ n�Z n
0FX(x)dx
= nFX(n)� n+
Z n
0[1� FX(x)]dx
46
= n[FX(n)� 1] +
Z n
0[1� FX(x)]dx
= �n[1� FX(n)] +
Z n
0[1� FX(x)]dx
= �nP (X > n) +
Z n
0[1� FX(x)]dx
X�0= �n1P (jXj > n) +
Z n
0[1� FX(x)]dx
=) E(X1) = limn!1[�n
1P (jXj > n) +
Z n
0[1� FX(x)]dx]
Th: 3:1:9= 0 + lim
n!1
Z n
0[1� FX(x)]dx
=
Z 1
0[1� FX(x)]dx
Thus, the existence of E(X) implies that
Z 1
0[1�FX (x)]dx is �nite and that both sides are identical.
We still have to show the converse implication:
If
Z 1
0[1 � FX(x)]dx is �nite, then E(X) exists, i.e., E(j X j) = E(X) < 1, and both sides are
identical. It isZ n
0xfX(x)dx
X �0=
Z n
0j x j fX(x)dx = �n[1� FX(n)] +
Z n
0[1� FX(x)]dx
as seen above.
Since �n[1� FX(n)] � 0, we get
Z n
0j x j fX(x)dx �
Z n
0[1� FX(x)]dx �
Z 1
0[1� FX(x)]dx <1 8n
Thus,
limn!1
Z n
0j x j fX(x) =
Z 1
0j x j fX(x)dx �
Z 1
0[1� FX(x)]dx <1
=) E(X) exists and is identical to
Z 1
0[1� FX(x)]dx as seen above.
47
Corollary 3.1.12:
E(j X js) = s
Z 1
0ys�1
P (j X j> y)dy
Proof:
E(j X js) Lemma 3:1:11=
Z 1
0[1� FjXjs(z)]dz =
Z 1
0P (j X js> z)dz.
Let z = ys. Then dz
dy = sys�1 and dz = sy
s�1dy. Therefore,
Z 1
0P (j X js> z)dz =
Z 1
0P (j X js> y
s)sys�1dy
= s
Z 1
0ys�1
P (j X js> ys)dy
monotonic "= s
Z 1
0ys�1
P (j X j> y)dy
Proof (of Theorem 3.1.10):
For any given � > 0, choose N such that the tail probability P (j X j> n) < �nt
8n � N .
E(j X js) Cor: 3:1:12= s
Z 1
0ys�1
P (j X j> y)dy
= s
Z N
0ys�1
P (j X j> y)dy + s
Z 1
Nys�1
P (j X j> y)dy
�Z N
0sy
s�1 � 1 dy + s
Z 1
Nys�1 �
ytdy
= ys jN0 + s�
Z 1
Nys�1 1
ytdy
= Ns + s�
Z 1
Nys�1�t
dy
It is
Z 1
Nycdy =
(1
c+1yc+1 j1N ; c 6= �1
ln y j1N ; c = 1
=
(1; c � �1� 1c+1N
c+1<1; c < �1
Thus, for E(j X js) < 1, it must hold that s � 1 � t < �1, or equivalently, s < t. So
E(j X js) < 1, i.e., it exists, for every s with 0 < s < t for a rv X with a distribution such
that limn!1n
tP (j X j> n) = 0 for some t > 0.
48
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 19: Wednesday 10/13/1999
Scribe: J�urgen Symanzik, Rich Madsen
Theorem 3.1.13:
Let X be a rv such that
limk!1
P (j X j> �k)
P (j X j> k)= 0 8� > 1:
Then, all moments of X exist.
Proof:
� For � > 0; we select some k0 such that
P (j X j> �k)
P (j X j> k)< � 8k � k0:
� Select k1 such that P (j X j> k) < � 8k � k1:
� Select N = max(k0; k1).
� If we have some �xed positive integer r:
P (j X j> �rk)
P (j X j> k)=
P (j X j> �k)
P (j X j> k)� P (j X j> �
2k)
P (j X j> �k)� P (j X j> �
3k)
P (j X j> �2k)� : : : � P (j X j> �
rk)
P (j X j> �r�1k)
=) P (j X j> �rk)
P (j X j> k)=
P (j X j> �k)
P (j X j> k)�P (j X j> � � (�k))P (j X j> 1 � (�k)) �
P (j X j> � � (�2k))
P (j X j> 1 � (�2k))�: : :�P (j X j> � � (�r�1k))
P (j X j> 1 � (�r�1k))� Note: Each of these r terms on the right side is > � by our original statement of selecting some
k0 such thatP (jXj>�k)P (jXj>k) < � 8k � k0 and since � > 1 and therefore �nk � k0.
� Now we get for our entire expression thatP (jXj>�rk)P (jXj>k) � �
r for k � N (since in this case also
k � k0) and � > 1:
� Overall, we have P (j X j> �rk) � �
rP (j X j> k) � �
r+1 for k � N (since in this case also
k � k1).
� For a �xed positive integer n:
E(j X jn) Cor:3:1:12= n �
1Z0
xn�1P(j X j> x)dx = n
NZ0
xn�1P(j X j> x)dx + n
1ZN
xn�1P(j X j> x)dx
� We know that:
n
NZ0
xn�1
P (j X j> x)dx �NZ0
nxn�1
dx = xn jN0 = N
n<1
49
but is
n
1ZN
xn�1
P (j X j> x)dx <1 ?
� To check the second part, we use:
1ZN
xn�1
P (j X j> x)dx =1Xr=1
�rNZ�r�1N
xn�1
P (j X j> x)dx
� We know that:�rNZ
�r�1N
xn�1
P (j X j> x)dx � �r
�rNZ�r�1N
xn�1
dx
This step is possible since �r � P (j X j� �
r�1N) � P (j X j> x) � P (j X j� �
rN)
8x 2 (�r�1N;�rN) and N = max(k0; k1).
� Since (�r�1N)n�1 � xn�1 � (�rN)n�1 8x 2 (�r�1N;�rN), we get:
�r
�rNZ�r�1N
xn�1
dx � �r(�rN)n�1
�rNZ�r�1N
1dx � �r(�rN)n�1(�rN) � �
r(�rN)n
� Now we go back to our original inequality:
1ZN
xn�1
P (j X j> x)dx �1Xr=1
�r
�rNZ�r�1N
xn�1
dx �1Xr=1
�r(�rN)n = N
n1Xr=1
(� � �n)r
=N
n��
n
1� ��nif ��n < 1 or, equivalently, if � <
1
�n
� Since Nn��n
1���n is �nite, all moments E(j X jn) exist.
50
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 20: Friday 10/15/1999
Scribe: J�urgen Symanzik, Hanadi B. Eltahir
3.2 Generating Functions
De�nition 3.2.1:
Let X be a rv with cdf FX . The moment generating function (mgf) of X is de�ned as
MX(t) = E(etX )
provided that this expectation exists in an (open) interval around 0, i.e., for �h < t < h for some
h > 0.
Theorem 3.2.2:
If a rv X has a mgf MX(t) that exists for �h < t < h for some h > 0, then
E(Xn) =M(n)X (0) =
dn
dtnMX(t) jt=0 :
Proof:
We assume that we can di�erentiate under the integral sign. If, and when, this really is true will
be discussed later in this section.
d
dtMX(t) =
d
dt
Z 1
�1etxfX(x)dx
=
Z 1
�1(@
@tetxfX(x))dx
=
Z 1
�1xe
txfX(x)dx
= E(XetX )
Evaluating this at t = 0, we get: ddtMX(t) jt=0= E(X)
By iteration, we get for n � 2:
dn
dtnMX(t) =
d
dt
dn�1
dtn�1MX(t)
!
=d
dt
�Z 1
�1xn�1
etxfX(x)dx
�
=
Z 1
�1(@
@txn�1
etxfX(x))dx
51
=
Z 1
�1xnetxfX(x)dx
= E(XnetX)
Evaluating this at t = 0, we get: dn
dtnMX(t) jt=0= E(Xn)
Example 3.2.3:
X � U(a; b); fX(x) =1
b�a � I[a;b](x).Then,
MX(t) =
Z b
a
etx
b� adx =
etb � e
ta
t(b� a)
MX(0) =0
0
L'Hospital=
betb � ae
ta
b� a
= 1
So MX(0) = 1 and since etb�etat(b�a) is continuous, it also exists in an open interval around 0 (in fact,
it exists for every t 2 IR).
M0X(t) =
(betb � aeta)t(b� a)� (etb � e
ta)(b� a)
t2(b� a)2
=t(betb � ae
ta)� (etb � eta)
t2(b� a)
=) E(X) = M0X(0) =
0
0
L'Hospital=
betb � ae
ta + tb2etb � ta
2eta � be
tb + aeta
2t(b� a)
�����t=0
=tb
2etb � ta
2eta
2t(b� a)
�����t=0
=0
0
L'Hospital=
b2etb � a
2eta + tb
3etb � ta
3eta
2(b� a)
�����t=0
=b2 � a
2
2(b� a)
=b+ a
2
52
Note:
In the previous example, we made use of L'Hospital's rule. This rule gives conditions under
which we can resolve inde�nite expressions of the type \�0�0" and \�1�1".
(i) Let f and g be functions that are di�erentiable in an open interval around x0, say in
(x0 � �; x0 + �); but not necessarily di�erentiable in x0. Let f(x0) = g(x0) = 0 and g0(x) 6= 0
8x 2 (x0 � �; x0 + �) � fx0g. Then, limx!x0
f0(x)g0(x)
= A implies that also limx!x0
f(x)
g(x)= A. The
same holds for the cases limx!x0
f(x) = limx!x0
g(x) =1 and x! x+0 or x! x
�0 .
(ii) Let f and g be functions that are di�erentiable for x > a (a > 0). Let limx!1 f(x) = lim
x!1 g(x) =
0 and limx!1 g
0(x) 6= 0. Then, limx!1
f0(x)g0(x)
= A implies that also limx!1
f(x)
g(x)= A.
(iii) We can iterate this process as long as the required conditions are met and derivatives exist,
e.g., if the �rst derivatives still result in an inde�nite expression, we can look at the second
derivatives, then at the third derivatives, and so on.
(iv) It is recommended to keep expressions as simple as possible. If we have identical factors in the
numerator and denominator, we can exclude them from both and continue with the simpler
functions.
(v) Inde�nite expressions of the form \0 �1" can be handled by rearranging them to \ 01=1" and
limx!�1
f(x)
g(x)can be handled by use of the rules for lim
x!1f(�x)g(�x) .
Note:
The following Theorems provide us with rules that tell us when we can di�erentiate under the
integral sign. Theorem 3.2.4 relates to �nite integral bounds a(�) and b(�) and Theorems 3.2.5 and
3.2.6 to in�nite bounds.
Theorem 3.2.4: Leibnitz's Rule
If f(x; �); a(�), and b(�) are di�erentiable with respect to � (for all x) and �1 < a(�) < b(�) <1,
thend
d�
Z b(�)
a(�)f(x; �)dx = f(b(�); �)
d
d�b(�)� f(a(�); �)
d
d�a(�) +
Z b(�)
a(�)
@
@�f(x; �)dx:
The �rst 2 terms are vanishing if a(�) and b(�) are constant in �.
Proof:
Uses the Fundamental Theorem of Calculus and the chain rule.
53
Theorem 3.2.5: Lebesque's Dominated Convergence Theorem
Let g be an integrable function such that
Z 1
�1g(x)dx < 1. If j fn j� g almost everywhere (i.e.,
except for a set of Borel{measure 0) and if fn ! f almost everywhere, then fn and f are integrable
and Zfn(x)dx!
Zf(x)dx:
Note:
If f is di�erentiable with respect to �, then
@
@�f(x; �) = lim
�!0
f(x; � + �) � f(x; �)
�
and Z 1
�1
@
@�f(x; �)dx =
Z 1
�1lim�!0
f(x; � + �)� f(x; �)
�dx
while@
@�
Z 1
�1f(x; �)dx = lim
�!0
Z 1
�1
f(x; � + �)� f(x; �)
�dx
Theorem 3.2.6:
Let fn(x; �0) =f(x;�0+�n)�f(x;�0)
�nfor some �0. Suppose there exists an integrable function g(x) such
that
Z 1
�1g(x)dx <1 and j fn(x; �) j� g(x) 8x, then
�d
d�
Z 1
�1f(x; �)dx
��=�0
=
Z 1
�1
�@
@�f(x; �) j�=�0
�dx:
Usually, if f is di�erentiable for all �, we write
d
d�
Z 1
�1f(x; �)dx =
Z 1
�1
@
@�f(x; �)dx:
Corollary 3.2.7:
Let f(x; �) be di�erentiable for all �. Suppose there exists an integrable function g(x; �) such thatZ 1
�1g(x; �)dx <1 and
��� @@�f(x; �) j�=�0��� � g(x; �) 8x 8�0 in some �{neighborhood of �, then
d
d�
Z 1
�1f(x; �)dx =
Z 1
�1
@
@�f(x; �) j�=�0 dx:
54
More on Moment Generating Functions
Consider ���� @@tetxfX(x) jt=t0
���� =j x j et0xfX(x) for j t0 � t j� �0:
Choose t; �0 small enough such that t+ �0 2 (�h; h) and t� �0 2 (�h; h). Then,���� @@tetxfX(x) jt=t0
���� � g(x; t)
where
g(x; t) =
(j x j e(t+�0)xfX(x); x � 0
j x j e(t��0)xfX(x); x < 0
To verifyRg(x; t)dx <1, we need to know fX(x).
Suppose mgf MX(t) exists for j t j� h for some h > 1. Then j t+ �0 + 1 j< h and j t� �0 � 1 j< h.
Since j x j� ejxj 8x, we get
g(x; t) �(
e(t+�0+1)x
fX(x); x � 0
e(t��0�1)xfX(x); x < 0
Then,
Z 1
0g(x; t)dx �MX(t+�0+1) <1 and
Z 0
�1g(x; t)dx �MX(t��0�1) <1 and, therefore,Z 1
�1g(x)dx <1.
Together with Corollary 3.2.7, this establishes that we can di�erentiate under the integral in the
Proof of Theorem 3.2.2.
If h � 1, we may need to check more carefully to see if the condition holds.
Note:
If MX(t) exists for t 2 (�h; h), then we have an in�nite collection of moments.
Does a collection of integer moments fmk : k = 1; 2; 3; : : :g completely characterize the distribution,i.e., cdf, of X? | Unfortunately not, as Example 3.2.8 shows.
55
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 21: Monday 10/18/1999
Scribe: J�urgen Symanzik
Example 3.2.8:
Let X1 and X2 be rv's with pdfs
fX1(x) =
1p2�
1
xexp(�1
2(log x)2) � I(0;1)(x)
and
fX2(x) = fX1
(x) � (1 + sin(2� log x)) � I(0;1)(x)
It is E(Xr1 ) = E(Xr
2 ) = er2=2 for r = 0; 1; 2; : : : as you have to show in the Homeworks.
Two di�erent pdfs/cdfs have the same moment sequence! What went wrong? In this example,
MX1(t) does not exist as shown in the Homeworks!
Theorem 3.2.9:
Let X and Y be 2 rv's with cdf's FX and FY for which all moments exist.
(i) If FX and FY have bounded support, then FX(u) = FY (u) 8u i� E(Xr) = E(Y r) for
r = 0; 1; 2; : : :.
(ii) If both mgf's exist, i.e., MX(t) = MY (t) for t in some neighborhood of 0, then FX(u) =
FY (u) 8u.
Note:
The existence of moments is not equivalent to the existence of a mgf as seen in Example 3.2.8 above
and some of the Homework assignments.
Theorem 3.2.10:
Suppose rv's fXig1i=1 have mgf's MXi(t) and that lim
i!1MXi
(t) = MX(t) 8t 2 (�h; h) for someh > 0 and that MX(t) itself is a mgf. Then, there exists a cdf FX whose moments are determined
by MX(t) and for all continuity points x of FX(x) it holds that limi!1
FXi(x) = FX(x), i.e., the
convergence of mgf's implies the convergence of cdf's.
Proof:
Uniqueness of Laplace transformations, etc.
56
Theorem 3.2.11:
For constants a and b, the mgf of Y = aX + b is
MY (t) = ebtMX(at);
given that MX(t) exists.
Proof:
MY (t) = E(e(aX+b)t)
= E(eaXtebt)
= ebtE(eXat)
= ebtMX(at)
3.3 Complex{Valued Random Variables and Characteristic Functions
Recall the following facts regarding complexd numbers:
i0 = +1; i =
p�1; i2 = �1; i3 = �i; i4 = +1; etc.
in the planar Gauss'ian number plane it holds that i = (0; 1)
z = a+ ib = r(cos�+ i sin�)
r =j z j=pa2 + b2
tan� = ba
Euler's Relation: z = r(cos�+ i sin�) = rei�
Mathematical Operations on Complex Numbers:
z1 � z2 = (a1 � a2) + i(b1 � b2)
z1 � z2 = r1r2ei(�1+�2) = r1r2(cos(�1 + �2) + i sin(�1 + �2))
z1z2= r1
r2ei(�1��2) = r1
r2(cos(�1 � �2) + i sin(�1 � �2))
Moivre's Theorem: zn = (r(cos�+ i sin�))n = rn(cos(n�) + i sin(n�))
npz = n
pa+ ib = n
pr
�cos(�+k�360
0
n ) + i sin(�+k�3600
n )�for k = 0; 1; : : : ; (n � 1) and the main value
for k = 0
ln z = ln(a+ ib) = ln(j z j) + i�� i2n� where � = arctan ba and the main value for n = 0
57
Conjugate Complex Numbers:
For z = a+ ib, we de�ne the conjugate complex number z = a� ib. It holds:
z = z
z = z i� z 2 IR
z1 � z2 = z1 � z2
z1 � z2 = z1 � z2�z1z2
�= z1
z2
z � z = a2 + b
2
Re(z) = a = 12 (z + z)
Im(z) = b = 12i (z � z)
j z j=pa2 + b2 =
pz � z
De�nition 3.3.1:
Let (; L; P ) be a probability space and X and Y real{valued rv's, i.e., X;Y : (; L)! (IR;B)
(i) Z = X + iY : (; L)! (CI;BCI) is called a complex{valued random variable (CI-rv).
(ii) If E(X) and E(Y ) exist, then E(Z) is de�ned as E(Z) = E(X) + iE(Y ) 2 CI.
Note:
E(Z) exists i� E(j X j) and E(j Y j) exist. It also holds that if E(Z) exists, then j E(Z) j� E(j Z j)(see Homework).
De�nition 3.3.2:
Let X be a real{valued rv on (; L; P ). Then, �X(t) : IR! CI with �X(t) = E(eitX ) is called the
characteristic function of X.
Note:
(i) �X(t) =
Z 1
�1eitxfX(x)dx =
Z 1
�1cos(tx)fX(x)dx+ i
Z 1
�1sin(tx)fX(x)dx if X is continuous.
(ii) �X(t) =Xx2X
eitxP (X = x) =
Xx2X
cos(tx)P (X = x)+iXx2X
sin(tx)P (X = x)(x) ifX is discrete
and X is the support of X.
(iii) �X(t) exists for all real{valued rv's X since j eitx j= 1.
58
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 22: Wednesday 10/20/1999
Scribe: J�urgen Symanzik, Bill Morphet
Theorem 3.3.3:
Let �X be the characteristic function of a real{valued rv X. Then it holds:
(i) �X(0) = 1.
(ii) j �X(t) j� 1 8t 2 IR.
(iii) �X is uniformly continuous, i.e., 8� > 0 9� > 0 8t1; t2 2 IR :j t1�t2 j< � )j �(t1)��(t2) j< �.
(iv) �X is a positive de�nite function, i.e., 8n 2 IN 8�1; : : : ; �n 2 CI 8t1; : : : ; tn 2 IR :
nXl=1
nXj=1
�l�j�X(tl � tj) � 0.
(v) �X(t) = �X(�t).
(vi) If X is symmetric around 0, i.e., if X has a pdf that is symmetric around 0, then �X(t) 2IR 8t 2 IR.
(vii) �aX+b(t) = eitb�X(at).
Proof:
See Homework for parts (i), (ii), (iv), (v), (vi), and (vii).
Part (iii):
Known conditions:
(i) Let � > 0.
(ii) 9 a > 0 : P (�a < X < +a) > 1� �4 and P (j X j> a) < �
4
(iii) 9 � > 0 : j e{(t0�t)x � 1 j< �2 8x s.t. j x j< a and 8(t0 � t) s.t. 0 < (t0 � t) < �.
This third condition holds since j e{0 � 1 j= 0 and the exponential function is continuous.
Therefore, if we select (t0 � t) and x small enough, j e{(t0�t)x � 1 j will be < �2 for a given �.
59
Let t; t0 2 IR, t < t0, and t
0 � t < �. Then,
j �X(t0)��X(t) j = j
Z +1
�1e{t0x
fX(x)dx�Z +1
�1e{txfX(x)dx j
= jZ +1
�1(e{t
0x � e{tx)fX(x)dx j
= jZ �a
�1(e{t
0x � e{tx)fX(x)dx+
Z +a
�a(e{t
0x � e{tx)fX(x)dx+
Z +1
+a(e{t
0x � e{tx)fX(x)dx j
� jZ �a
�1(e{t
0x� e{tx)fX(x)dx j + j
Z +a
�a(e{t
0x� e{tx)fX(x)dx j + j
Z +1
+a(e{t
0x� e{tx)fX(x)dx j
We now take a closer look at the �rst and third of these absolute integrals. It is:
jZ �a
�1(e{t
0x � e{tx)fX(x)dx j = j
Z �a
�1e{t0x
fX(x)dx�Z �a
�1e{txfX(x)dx j
� jZ �a
�1e{t0x
fX(x)dx j + jZ �a
�1e{txfX(x)dx j
�Z �a
�1j e{t0x j fX(x)dx +
Z �a
�1j e{tx j fX(x)dx
(A)=
Z �a
�11fX(x)dx+
Z �a
�11fX(x)dx
=
Z �a
�12fX(x)dx.
(A) holds due to Note (iii) that follows De�nition 3.3.2.
Similarly,
jZ +1
+a(e{t
0x � e{tx)fX(x)dx j�
Z +1
+a2fX(x)dx
Returning to the main part of the proof, we get
j �X(t0)��X(t) j �
Z �a
�12fX(x)dx + j
Z +a
�a(e{t
0x � e{tx)fX(x)dx j +
Z +1
+a2fX(x)dx
= 2
�Z �a
�1fX(x)dx+
Z +1
+afX(x)dx
�+ j
Z +a
�a(e{t
0x � e{tx)fX(x)dx j
= 2P (j X j> a) + jZ +a
�a(e{t
0x � e{tx)fX(x)dx j
60
Condition (ii)
� 2�
4+ j
Z +a
�a(e{t
0x � e{tx)fX(x)dx j
=�
2+ j
Z +a
�ae{tx(e{(t
0�t)x � 1)fX(x)dx j
� �
2+
Z +a
�aj e{tx(e{(t0�t)x � 1) j fX(x)dx
� �
2+
Z +a
�aj e{tx j � j (e{(t0�t)x � 1) j fX(x)dx
(B)
� �
2+
Z +a
�a1�
2fX(x)dx
� �
2+
Z +1
�1
�
2fX(x)dx
=�
2+�
2
= �
(B) holds due to Note (iii) that follows De�nition 3.3.2 and due to condition (iii).
Theorem 3.3.4: Bochner's Theorem
Let � : IR ! CI be any function with properties (i), (ii), (iii), and (iv) from Theorem 3.3.3. Then
there exists a real{valued rv X with �X = �.
Theorem 3.3.5:
Let X be a real{valued rv and E(Xk) exists for an integer k. Then, �X is k times di�erentiable
and �(k)X (t) = i
kE(Xk
eitX). In particular for t = 0, it is �
(k)X (0) = i
kmk.
Theorem 3.3.6:
Let X be a real{valued rv with characteristic function �X and let �X be k times di�erentiable,
where k is an even integer. Then the kth moment of X, mk, exists and it is �(k)X (0) = i
kmk.
61
Theorem 3.3.7: Levy's Theorem
Let X be a real{valued rv with cdf FX and characteristic function �X . Let a; b 2 IR, a < b. If
P (X = a) = P (X = b) = 0, i.e., FX is continuous in a and b, then
F (b)� F (a) =1
2�
Z 1
�1
e�ita � e
�itb
it�X(t)dt:
Theorem 3.3.8:
Let X and Y be a real{valued rv with characteristic functions �X and �Y . If �X = �Y , then X
and Y are identically distributed.
Theorem 3.3.9:
Let X be a real{valued rv with characteristic function �X such that
Z 1
�1j �X(t) j dt <1. Then
X has pdf
fX(x) =1
2�
Z 1
�1e�itx�X(t)dt:
Theorem 3.3.10:
Let X be a real{valued rv with mgf MX(t), i.e., the mgf exists. Then �X(t) =MX(it).
Theorem 3.3.11:
Suppose real{valued rv's fXig1i=1 have cdf's fFXig1i=1 and characteristic functions f�Xi
(t)g1i=1. If
limi!1
�Xi(t) = �X(t) 8t 2 (�h; h) for some h > 0 and �X(t) is itself a characteristic function
(of a rv X with cdf FX), then limi!1
FXi(x) = FX(x) for all continuity points x of FX(x), i.e., the
convergence of characteristic functions implies the convergence of cdf's.
62
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 23: Friday 10/22/1999
Scribe: J�urgen Symanzik, Rich Madsen
Theorem 3.3.12:
Characteristic functions for some well{known distributions:
Distribution �X(t)
(i) X � Dirac(c) eitc
(ii) X � Bin(1; p) 1 + p(eit � 1)
(iii) X � Poisson(c) exp(c(eit � 1))
(iv) X � U(a; b) eitb�eita(b�a)it
(v) X � N(0; 1) exp(�t2=2)
(vi) X � N(�; �2) eit� exp(��2t2=2)
(vii) X � �(p; q) (1� itq )�p
(viii) X � Exp(c) (1� itc)�1
(ix) X � �2n (1� 2it)�n=2
Proof:
(i) �X(t) = E(eitX ) = eitcP (X = c) = e
itc
(ii) �X(t) =1X
k=0
eitkP (X = k) = e
it0(1� p) + eit1p = 1 + p(eit � 1)
(iii) �X(t) =Xn2IN0
eitn � c
n
n!e�c = e
�c1Xn=0
1
n!(c � eit)n = e
�c � ec�eit = ec(eit�1) since
1Xn=0
xn
n!= e
x
(iv) �X(t) =1
b�a
Z b
aeitxdx =
1
b� a
"eitx
it
#ba
=eitb � e
ita
(b� a)it
(v) X � N(0; 1) is symmetric around 0
) �X(t) is real since there is no imaginary part according to Theorem 3.3.3 (vi)
) �X(t) =1p2�
Z 1
�1cos(tx)e
�x2
2 dx
63
Since E(X) exists, �X(t) is di�erentiable according to Theorem 3.3.5 and the following holds:
�0X(t) = Re(�0X(t))
= Re
0B@Z 1
�1ix e
itx|{z}cos(tx)+i sin(tx)
1p2�
e�x2
2 dx
1CA
= Re
�Z 1
�1ix cos(tx)
1p2�
e�x2
2 dx+
Z 1
�1�x sin(tx) 1p
2�e�x2
2 dx
�
=1p2�
Z 1
�1(� sin(tx))| {z }
u
xe�x2
2| {z }v0
dx j u0 = �t cos(tx) and v = �e�x2
2
=1p2�
sin(tx)e�x2
2 j1�1| {z }=0 since sin is odd
� 1p2�
Z 1
�1(�t cos(tx))(�e
�x2
2 )dx
= �t 1p2�
Z 1
�1cos(tx)e
�x2
2 dx
= �t�X(t)
Thus, �0X(t) = �t�X(t). It follows that�0
X(t)
�X(t) = �t and by integrating both sides, we get
ln j �X(t) j= �12t2 + c with c 2 IR.
For t = 0, we know that �X(0) = 1 by Theorem 3.3.3 (i) and ln j �X(0) j= 0. It follows that
0 = 0 + c. Therefore, c = 0 and j �X(t) j= e� 1
2t2 .
If we take t = 0, then �X(0) = 1 by Theorem 3.3.3 (i). Since �X is continuous, �X must take
the value 0 before it can eventually take a negative value. However, since e�12t2> 0 8t 2 IR,
�X cannot take 0 as a possible value and therefore cannot pass into the negative numbers.
So, it must hold that �X(t) = e� 12t2 8t 2 IR:
(vi) For � > 0; � 2 IR, we know that if X � N(0; 1), then �X + � � N(�; �2). By Theorem 3.3.3
(vii) we have
��X+�(t) = eit��X(�t) = e
it�e� 12�2t2
:
(vii)
�X(t) =
Z 1
0eitx (p; q; x)dx
=
Z 1
0
qp
�(p)xp�1
e�(q�it)x
dx
=qp
�(p)(q � it)�p
Z 1
0((q � it)x)p�1e�(q�it)x(q � it)dx j u = (q � it)x; du = (q � it)dx
64
=qp
�(p)(q � it)�p
Z 1
0(u)p�1e�udu| {z }
=�(p)
= qp(q � it)�p
=(q � it)�p
q�p
=
�q � it
q
��p
=
�1� it
q
��p
(viii) Since an Exp(c) distribution is a �(1; c) distribution, we get for X � Exp(c) = �(1; c):
�X(t) = (1� it
c)�1
(ix) Since a �2n distribution (for n 2 IN) is a �(n2 ;12 ) distribution, we get for X � �
2n = �(n2 ;
12):
�X(t) = (1� it
1=2)�n=2 = (1� 2it)�n=2
Example 3.3.13:
Since we know that m1 = E(X) and m2 = E(X2) exist for X � Bin(1; p), we can determine these
moments according to Theorem 3.3.5 using the characteristic function.
It is
�X(t) = 1 + p(eit � 1)
�0X(t) = pieit
�0X(0) = pi
) m1 =�0X(0)
i=
pi
i= p = E(X)
�00X(t) = pi2eit
�00X(0) = pi2
) m2 =�00X(0)i2
=pi
2
i2= p = E(X2)
) V ar(X) = E(X2)� (E(X))2 = p� p2 = p(1� p)
65
Note:
The restriction
Z 1
�1j �X(t) j dt <1 in Theorem 3.3.9 works in such a way that we don't end up
with a (non{existing) pdf if X is a discrete rv. For example,
� X � Dirac(c): Z 1
�1j �X(t) j dt =
Z 1
�1j eitc j dt
=
Z 1
�11dt
= x j1�1
which is unde�ned.
� Also for X � Bin(1; p):Z 1
�1j �X(t) j dt =
Z 1
�1j 1 + p(eit � 1) j dt
=
Z 1
�1j peit � (p� 1) j dt
�Z 1
�1j j peit j � j (p� 1) j j dt
�Z 1
�1j peit j dt�
Z 1
�1j (p� 1) j dt
= p
Z 1
�11dt� (1� p)
Z 1
�11 dt
= (2p� 1)
Z 1
�11 dt
= (2p� 1)x j1�1
which is unde�ned for p 6= 1=2.
If p = 1=2, we haveZ 1
�1j peit � (p� 1) j dt = 1=2
Z 1
�1j eit + 1 j dt
= 1=2
Z 1
�1j cos t+ i sin t+ 1 j dt
= 1=2
Z 1
�1
q(cos t+ 1)2 + (sin t)2 dt
= 1=2
Z 1
�1
qcos2 t+ 2 cos t+ 1 + sin2 t dt
= 1=2
Z 1
�1
p2 + 2 cos t dt
which also does not exist.
66
� Otherwise, X � N(0; 1):
Z 1
�1j �X(t) j dt =
Z 1
�1exp(�t2=2)dt
=p2�
Z 1
�1
1p2�
exp(�t2=2)dt
=p2�
< 1
67
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 24: Monday 10/25/1999
Scribe: J�urgen Symanzik, Hanadi B. Eltahir
3.4 Probability Generating Functions
De�nition 3.4.1:
Let X be a discrete rv which only takes non{negative integer values, i.e., pk = P (X = k), and1Xk=0
pk = 1. Then, the probability generating function (pgf) of X is de�ned as
G(s) =1Xk=0
pksk:
Theorem 3.4.2:
G(s) converges for j s j< 1.
Proof:
j G(s) j�1Xk=0
j pksk j�1Xk=0
j pk j= 1
Theorem 3.4.3:
Let X be a discrete rv which only takes non{negative integer values and has pgf G(s). Then it
holds:
P (X = k) =1
k!
dk
dskG(s) js=0
Theorem 3.4.4:
Let X be a discrete rv which only takes non{negative integer values and has pgf G(s). If E(X)
exists, then it holds:
E(X) =d
dsG(s) js=1
68
De�nition 3.4.5:
The kth factorial moment of X is de�ned as
E[X(X � 1)(X � 2) � : : : � (X � k + 1)]
if this expectation exists.
Theorem 3.4.6:
Let X be a discrete rv which only takes non{negative integer values and has pgf G(s). If
E[X(X � 1)(X � 2) � : : : � (X � k + 1)] exists, then it holds:
E[X(X � 1)(X � 2) � : : : � (X � k + 1)] =dk
dskG(s) js=1
Note:
Similar to the Cauchy distribution for the continuous case, there exist discrete distributions where
the mean (or higher moments) do not exist. See Homework.
3.5 Moment Inequalities
Theorem 3.5.1:
Let h(X) be a non{negative Borel{measurable function of a rv X. If E(h(X)) exists, then it holds:
P (h(X) � �) � E(h(X))
�8� > 0
Proof:
Continuous case only:
E(h(X)) =
Z 1
�1h(x)fX(x)dx
=
ZAh(x)fX(x)dx+
ZAC
h(x)fX(x)dx j where A = fx : h(x) � �g
�ZAh(x)fX(x)dx
�ZA�fX(x)dx
= �P (h(X) � �) 8� > 0
Therefore, P (h(X) � �) � E(h(X))� 8� > 0.
69
Corollary 3.5.2: Markov's Inequality
Let h(X) =j X jr and � = kr where r > 0 and k > 0. If E(j X jr) exists, then it holds:
P (j X j� k) � E(j X jr)kr
Proof:
Since P (j X j� k) = P (j X jr� kr) for k > 0, it follows using Theorem 3.5.1:
P (j X j� k) = P (j X jr� kr)
Th:3:5:1� E(j X jr)
kr
Corollary 3.5.3: Chebychev's Inequality
Let h(X) = (X � �)2 and � = k2�2 where E(X) = �, V ar(X) = �
2< 1, and k > 0. Then it
holds:
P (j X � � j> k�) � 1
k2
Proof:
Since P (j X � � j> k�) = P (j X � � j2> k2�2) for k > 0, it follows using Theorem 3.5.1:
P (j X � � j> k�) = P (j X � � j2> k2�2)
Th:3:5:1� E(j X � � j2)
k2�2=
V ar(X)
k2�2=
�2
k2�2=
1
k2
Theorem 3.5.4: Lyapunov Inequality
Let 0 < �n = E(j X jn) <1. For arbitrary k such that 2 � k � n, it holds that
(�k�1)1
k�1 � (�k)1k ;
i.e., (E(j X jk�1))1
k�1 � (E(j X jk)) 1k .
Proof:
Continuous case only:
Let Q(u; v) = E
�(u j X j
k�12 +v j X j
k+12 )2
�. Obviously, Q(u; v) � 0 8u; v 2 IR. Also,
Q(u; v) =
Z 1
�1(u j x j
k�12 +v j x j
k+12 )2fX(x)dx
= u2Z 1
�1j x jk�1 fX(x)dx+ 2uv
Z 1
�1j x jk fX(x)dx+ v
2Z 1
�1j x jk+1
fX(x)dx
= u2�k�1 + 2uv�k + v
2�k+1
� 0 8u; v 2 IR
70
Using the fact that Ax2 + 2Bxy + Cy2 � 0 8x; y 2 IR i� A > 0 and AC � B
2> 0 (see Rohatgi,
page 6, Section P2.4), we get with A = �k�1; B = �k, and C = �k+1:
�k�1�k+1 � �2k � 0
=) �2k � �k�1�k+1
=) �2kk � �
kk�1�
kk+1
This means that �21 � �0�2, �42 � �
21�
23 , �
63 � �
32�
34 , and so on. Multiplying these, we get:
k�1Yj=1
�2jj �
k�1Yj=1
�jj�1�
jj+1
= (�0�2)(�21�
23)(�
32�
34)(�
43�
45) : : : (�
k�2k�3�
k�2k�1)(�
k�1k�2�
k�1k )
= �0�k�2k�1�
k�1k
k�2Yj=1
�2jj
Dividing both sides byk�2Yj=1
�2jj , we get:
�2k�2k�1 � �0�
k�1k �
k�2k�1
�0=1=) �
kk�1 � �
k�1k
=) �1k�1 � �
k�1k
k
=) �
1k�1
k�1 � �
1k
k
71
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 25: Wednesday 10/27/1999
Scribe: J�urgen Symanzik
4 Random Vectors
4.1 Joint, Marginal, and Conditional Distributions
De�nition 4.1.1:
The vector X = (X1; : : : ;Xn)0 on (; L; P ) ! IR
n de�ned by X(!) = (X1(!); : : : ;Xn(!))0; ! 2 ,
is an n{dimensional random vector (n{rv) if X�1(I) = f! : X1(!) � a1; : : : ;Xn(!) � ang 2 L
for all n{dimensional intervals I = f(x1; : : : ; xn) : �1 < xi � ai; ai 2 IR 8i = 1; : : : ; ng.
Note:
It follows that if X1; : : : ;Xn are any n rv's on (; L; P ), then X = (X1; : : : ;Xn)0 is an n{rv on
(; L; P ) since for any I, it holds:
X�1(I) = f! : (X1(!); : : : ;Xn(!)) 2 Ig
= f! : X1(!) � a1; : : : ;Xn(!) � ang
=n\
k=1
f! : Xk(!) � akg| {z }2L| {z }
2L
De�nition 4.1.2:
For an n{rv X , a function F de�ned by
F (x) = P (X � x) = P (X1 � x1; : : : ; Xn � xn) 8x 2 IRn
is the joint cumulative distribution function of X.
Note:
(i) F is non{decreasing and right{continuous in each of its arguments xi.
(ii) limx!1F (x) = lim
x1!1;:::xn!1F (x) = 1 and limxk!�1
F (x) = 0 8x1; : : : ; xk�1; xk+1; : : : ; xn 2 IR.
72
However, conditions (i) and (ii) together are not su�cient for F to be a joint cdf. Instead we need
the conditions from the next Theorem.
Theorem 4.1.3:
A function F (x) = F (x1; : : : ; xn) is the joint cdf of some n{rv X i�
(i) F is non{decreasing and right{continuous with respect to each xi,
(ii) F (�1; x2; : : : ; xn) = F (x1;�1; x3; : : : ; xn) = : : : = F (x1; : : : ; xn�1;�1) = 0 and
F (1; : : : ;1) = 1, and
(iii) 8x 2 IRn 8�i > 0; i = 1; : : : n, the following inequality holds:
F (x+ �) �nXi=1
F (x1 + �1; : : : ; xi�1 + �i�1; xi; xi+1 + �i+1; : : : ; xn + �n)
+X
1�i<j�nF (x1 + �1; : : : ; xi�1 + �i�1; xi; xi+1 + �i+1; : : : ;
xj�1 + �j�1; xj ; xj+1 + �j+1; : : : ; xn + �n)
� : : :
+ (�1)nF (x)
� 0
Note:
We won't prove this Theorem but just see why we need condition (iii) for n = 2:
P (x1 < X � x2; y1 < Y � y2) =
P (X � x2; Y � y2)�P (X � x1; Y � y2)�P (X � x2; Y � y1) +P (X � x1; Y � y1) � 0
We will restrict ourselves to n = 2 for most of the next De�nitions and Theorems but those can
be easily generalized to n > 2. The term bivariate rv is often used to refer to a 2{rv and multi-
variate rv is used to refer to an n{rv, n � 2.
De�nition 4.1.4:
A 2{rv (X;Y ) is discrete if there exists a countable collection X of pairs (xi; yi) that has proba-
bility 1. Let pij = P (X = xi; Y = yj) > 0 8(xi; yj) 2 X. Then,Xi;j
pij = 1 and fpijg is the joint
probabiliy mass function of (X;Y ).
73
De�nition 4.1.5:
Let (X;Y ) be a discrete 2{rv with joint pmf fpijg. De�ne
pi� =1Xj=1
pij =1Xj=1
P (X = xi; Y = yj) = P (X = xi)
and
p�j =1Xi=1
pij =1Xi=1
P (X = xi; Y = yj) = P (Y = yj):
Then fpi�g is called the marginal probability mass function of X and fp�jg is called the
marginal probability mass function of Y .
De�nition 4.1.6:
A 2{rv (X;Y ) is continuous if there exists a non{negative function f such that
F (x; y) =
Z x
�1
Z y
�1f(u; v) dv du 8(x; y) 2 IR
2
where F is the joint cdf of (X;Y ). We call f the joint probability density function of (X;Y ).
Note:
If F is continuous at (x; y), then
d2F (x; y)
dx dy= f(x; y):
De�nition 4.1.7:
Let (X;Y ) be a continuous 2{rv with joint pdf f . Then fX(x) =
Z 1
�1f(x; y)dy is called the
marginal probability density function of X and fY (y) =
Z 1
�1f(x; y)dx is called themarginal
probability density function of Y .
Note:
(i) Z 1
�1fX(x)dx =
Z 1
�1
�Z 1
�1f(x; y)dy
�dx = F (1;1) = 1 =
Z 1
�1
�Z 1
�1f(x; y)dx
�dy =
Z 1
�1fY (y)dy
and fX(x) � 0 8x 2 IR and fY (y) � 0 8y 2 IR.
(ii) Given a 2{rv (X;Y ) with joint cdf F (x; y), how do we generate a marginal cdf
FX(x) = P (X � x) ? | The answer is P (X � x) = P (X � x;�1 < Y <1) = F (x;1).
74
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 26: Friday 10/29/1999
Scribe: J�urgen Symanzik
De�nition 4.1.8:
If FX(x1; : : : ; xn) = FX(x) is the joint cdf of an n{rv X = (X1; : : : ;Xn), then the marginal
cumulative distribution function of (Xi1 ; : : : ;Xik); 1 � k � n� 1; 1 � i1 < i2 < : : : < ik � n,
is given by
limxi!1;i6=i1;:::;ik
FX(x) = FX(1; : : : ;1; xi1 ;1; : : : ;1; xi2 ;1; : : : ;1; xik ;1; : : : ;1):
Note:
In De�nition 1.4.1, we de�ned conditional probability distributions in some probability space
(; L; P ). This de�nition extends to conditional distributions of 2{rv's (X;Y ).
De�nition 4.1.9:
Let (X;Y ) be a discrete 2{rv. If P (Y = yj) = p�j > 0, then the conditional probability mass
function of X given Y = yj (for �xed j) is de�ned as
pijj = P (X = xi j Y = yj) =P (X = xi; Y = yj)
P (Y = yj)=
pij
p�j:
Note:
For a continuous 2{rv (X;Y ) with pdf f , P (X � x j Y = y) is not de�ned. Let � > 0 and suppose
that P (y� � < Y � y+ �) > 0. For every x and every interval (y� �; y+ �], consider the conditional
probability of X � x given Y 2 (y � �; y + �]. We have
P (X � x j y � � < Y � y + �) =P (X � x; y � � < Y � y + �)
P (y � � < Y � y + �)
which is well{de�ned if P (y � � < Y � y + �) > 0 holds.
So, when does
lim�!0+
P (X � x j Y 2 (y � �; y + �])
exist? See the next de�nition.
75
De�nition 4.1.10:
The conditional cumulative distribution function of a rv X given that Y = y is de�ned to be
FXjY (x j y) = lim�!0+
P (X � x j Y 2 (y � �; y + �])
provided that this limit exists. If it does exist, the conditional probability density function
of X given that Y = y is any non{negative function fXjY (x j y) satisfying
FXjY (x j y) =Z x
�1fXjY (t j y)dt 8x 2 IR:
Note:
For �xed y, fXjY (x j y) � 0 and
Z 1
�1fXjY (x j y)dx = 1. So it is really a pdf.
Theorem 4.1.11:
Let (X;Y ) be a continuous 2{rv with joint pdf fX;Y . It holds that at every point (x; y) where f is
continuous and the marginal pdf fY (y) > 0, we have
FXjY (x j y) = lim�!0+
P (X � x; Y 2 (y � �; y + �])
P (Y 2 (y � �; y + �])
= lim�!0+
0BBB@
12�
Z x
�1
Z y+�
y��fX;Y (u; v)dv du
12�
Z y+�
y��fY (v)dv
1CCCA
=
Z x
�1fX;Y (u; y)du
fY (y)
=
Z x
�1
fX;Y (u; y)
fY (y)du:
Thus, fXjY (x j y) exists and equalsfX;Y (x;y)
fY (y) , provided that fY (y) > 0. Furthermore, since
Z x
�1fX;Y (u; y)du = fY (y)FXjY (x j y);
we get the following marginal cdf of X:
FX(x) =
Z 1
�1
�Z x
�1fX;Y (u; y)du
�dy =
Z 1
�1fY (y)FXjY (x j y)dy
76
Example 4.1.12:
Consider
fX;Y (x; y) =
(2; 0 < x < y < 1
0; otherwise
We calculate the marginal pdf's fX(x) and fY (y) �rst:
fX(x) =
Z 1
�1fX;Y (x; y)dy =
Z 1
x2dy = 2(1� x) for 0 < x < 1
and
fY (y) =
Z 1
�1fX;Y (x; y)dx =
Z y
02dx = 2y for 0 < y < 1
The conditional pdf's fY jX(y j x) and fXjY (x j y) are calculated as follows:
fY jX(y j x) =fX;Y (x; y)
fX(x)=
2
2(1� x)=
1
1� xfor x < y < 1 (where 0 < x < 1)
and
fXjY (x j y) =fX;Y (x; y)
fY (y)=
2
2y=
1
yfor 0 < x < y (where 0 < y < 1)
Thus, it holds that Y j X = x � U(x; 1) and X j Y = y � U(0; y), i.e., both conditional pdf's are
related to uniform distributions.
4.2 Independent Random Variables
Example 4.2.1: (from Rohatgi, page 119, Example 1)
Let f1; f2; f3 be 3 pdf's with cdf's F1; F2; F3 and let j � j� 1. De�ne
f�(x1; x2; x3) = f1(x1)f2(x2)f3(x3) � (1 + �(2F1(x1)� 1)(2F2(x2)� 1)(2F3(x3)� 1)):
We can show
(i) f� is a pdf for all � 2 [�1; 1].
(ii) ff� : �1 � � � 1g all have marginal pdf's f1; f2; f3.
See book for proof and further discussion | but when do the marginal distributions uniquely
determine the joint distribution?
77
De�nition 4.2.2:
Let FX;Y (x; y) be the joint cdf and FX(x) and FY (y) be the marginal cdf's of a 2{rv (X;Y ).
X and Y are independent i�
FX;Y (x; y) = FX(x)FY (y) 8(x; y) 2 IR2:
Lemma 4.2.3:
If X and Y are independent, a; b; c; d 2 IR, and a < b and c < d, then
P (a < X � b; c < Y � d) = P (a < X � b)P (c < Y � d):
Proof:
P (a < X � b; c < Y � d) = FX;Y (b; d) � FX;Y (a; d) � FX;Y (b; c) + FX;Y (a; c)
= FX(b)FY (d)� FX(a)FY (d)� FX(b)FY (c) + FX(a)FY (c)
= (FX(b)� FX(a))(FY (d)� FY (c))
= P (a < X � b)P (c < Y � d)
De�nition 4.2.4:
A collection of rv's X1; : : : ;Xn with joint cdf FX(x) and marginal cdf's FXi(xi) are mutually (or
completely) independent i�
FX(x) =nYi=1
FXi(xi) 8x 2 IR
n:
Note:
We often simply say that the rv's X1; : : : ;Xn are independent when we really mean that they are
mutually independent.
78
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 27: Monday 11/1/1999
Scribe: J�urgen Symanzik, Bill Morphet
Theorem 4.2.5: Factorization Theorem
(i) A necessary and su�cient condition for discrete rv's X1; : : : ;Xn to be independent is that
P (X = x) = P (X1 = x1; : : : ; Xn = xn) =nYi=1
P (Xi = xi) 8x 2 X
where X � IRn is the countable support of X .
(ii) For an absolutely continuous n{rv X = (X1; : : : ;Xn), X1; : : : ;Xn are independent i�
fX(x) = fX1;:::;Xn(x1; : : : ; xn) =nYi=1
fXi(xi);
where fX is the joint pdf and fX1; : : : ; fXn are the marginal pdfs of X.
Proof:
(i) Discrete case:
This is a generalization of Rohatgi, page 120, Theorem 1 to n-dimensional random vectors.
The Theorem is based on Lemma 4.2.3 (see also Rohatgi, page 120, Lemma 1) which gives
the cumulative probability of a completely bounded region in a two-dimensional support,
a < X � b; c < Y � d. The extension of the Lemma to n-dimensional space gives the cumu-
lative probability in a completely bounded region in an n-dimensional support (hyperspace).
Let X be a random vector whose components are independent random variables of the discrete
type with P (X = b) > 0. Lemma 4.2.3 extends to:
lima"b
P (a < X � b) = limai"bi 8i2f1;:::;ng
P (a1 < X1 � b1; a2 < X2 � b2; : : : ; an < Xn � bn)
= P (X1 = b1; X2 = b2; : : : ; Xn = bn)
= P (X = b)
indep:=
nYi=1
P (Xi = bi)
Before considering the converse factorization of the joint cdf of an n-rv, recall that indepen-
dence allows each value in the support of a component to combine with each of all possible
79
combinations of the other component values: a 3-dimensional vector where the �rst compo-
nent has support fx1a; x1b; x1cg, the second component has support fx2a; x2b; x2cg, and the
third component has support fx3a; x3b; x3cg, has 3� 3� 3 = 27 points in its support. Due to
independence, all these vectors can be arranged into three sets: the �rst set having x1a, the
second set having x1b, and the third set having x1c, with each set having 9 combinations of
x2 and x3.
=) F (x1:; x2:; x3:)
= P (X1 = x1a)F (x2:; x3:) + P (X1 = x1b)F (x2:; x3:) + P (X1 = x1c)F (x2:; x3:)
= F (x1:)F (x2:; x3:)
= F (x1:)[P (X2 = x2a)F (x3:) + P (X2 = x2b)F (x3:) + P (X2 = x2c)F (x3:)]
= F (x1:)[P (X2 = x2a) + P (X2 = x2b) + P (X2 = x2c)]F (x3:)
= F (x1:)F (x2:)F (x3:)
More generally, for n dimensions, let xi = (xi1; xi2; : : : ; xin); B = fxi : xi1 � x1; xi2 �x2; : : : ; xin � xng; B 2 X. Then it holds:
FX(x) =Xxi2B
P (X = xi)
=Xxi2B
P (X1 = xi1; X2 = xi2; : : : ; Xn = xin)
indep:=
Xxi2B
P (X1 = xi1)P (X2 = xi2; : : : ; Xn = xin)
=X
xi1 � x1; :::; xin � xn
P (X1 = xi1)P (X2 = xi2; : : : ; Xn = xin)
=X
xi1 � x1
P (X1 = xi1)X
xi2 � x2; :::; xin � xn
P (X2 = xi2; : : : Xn = xin)
= FX1(x1)
Xxi2 � x2; :::; xin � xn
P (X2 = xi2; : : : ; Xn = xin)
indep:= FX1
(x1)X
xi2 � x2
P (X2 = xi2)X
xi3 � x3; :::; xin � xn
P (X3 = xi3; : : : ; Xn = xin)
= FX1(x1)FX2
(x2)X
xi3 � x3; :::; xin � xn
P (X3 = xi3; : : : ; Xn = xin)
= : : :
= FX1(x1)FX2
(x2) : : : FXn(xn)
=nYi=1
FXi(xi)
(ii) Continuous case: Homework
80
Theorem 4.2.6:
X1; : : : ;Xn are independent i� P (Xi 2 Ai; i = 1; : : : ; n) =nYi=1
P (Xi 2 Ai) 8 Borel sets Ai 2 B
(i.e., rv's are independent i� all events involving these rv's are independent).
Proof:
Lemma 4.2.3 and de�nition of Borel sets.
Theorem 4.2.7:
Let X1; : : : ;Xn be independent rv's and g1; : : : ; gn be Borel{measurable functions. Then
g1(X1); g2(X2); : : : ; gn(Xn) are independent.
Proof:
Fg(X1);g(X2);:::;g(Xn)(h1; h2; : : : ; hn) = P (g(X1) � h1; g(X2) � h2; : : : ; g(Xn) � hn)
(�)= P (X1 2 g
�11 (�1; h1]; : : : ; Xn 2 g
�1n (�1; hn])
Th: 4:2:6=
nYi=1
P (Xi 2 g�1i (�1; hi])
=nYi=1
P (gi(Xi) � hi)
=nYi=1
Fgi(Xi)(hi)
(�) holds since g�11 (�1; h1] 2 B; : : : ; g�1n (�1; hn] 2 B
Theorem 4.2.8:
If X1; : : : ; Xn are independent, then also every subcollection Xi1 ; : : : ;Xik ; k = 2; : : : ; n � 1,
1 � i1 < i2 : : : < ik � n, is independent.
De�nition 4.2.9:
A set (or a sequence) of rv's fXng1n=1 is independent i� every �nite subcollection is independent.
Note:
Recall that X and Y are identically distributed i� FX(x) = FY (x) 8x 2 IR according to De�nition
2.2.5 and Theorem 2.2.6.
81
De�nition 4.2.10:
We say that fXng1n=1 is a set (or a sequence) of independent identically distributed (iid) rv's
if fXng1n=1 is independent and all Xn are identically distributed.
Note:
Recall that X and Y being identically distributed does not say that X = Y with probability 1. If
this happens, we say that X and Y are equivalent rv's.
Note:
We can also extend the de�ntion of independence to 2 random vectors Xn�1 and Yn�1: X and Y
are independent i� FX;Y (x; y) = FX(x)FY (y) 8x; y 2 IRn.
This does not mean that the components Xi of X or the components Yi of Y are independent.
However, it does mean that each pair of components (Xi; Yi) are independent, any subcollections
(Xi1 ; : : : ;Xik) and (Yj1 ; : : : ; Yjl) are independent, and any Borel{measurable functions f(X) and
g(Y ) are independent.
Corollary 4.2.11: (to Factorization Theorem 4.2.5)
If X and Y are independent rv's, then
FXjY (x j y) = FX(x) 8x;
and
FY jX(y j x) = FY (y) 8y:
4.3 Functions of Random Vectors
Theorem 4.3.1:
If X and Y are rv's on (; L; P )! IR, then
(i) X � Y is a rv.
(ii) XY is a rv.
(iii) If f! : Y (!) = 0g = �, then XY is a rv.
82
Theorem 4.3.2:
Let X1; : : : ; Xn be rv's on (; L; P )! IR. De�ne
MAXn = maxfX1; : : : ;Xng = X(n)
by
MAXn(!) = maxfX1(!); : : : ;Xn(!)g 8! 2
and
MINn = minfX1; : : : ;Xng = X(1) = �maxf�X1; : : : ;�Xng
by
MINn(!) = minfX1(!); : : : ;Xn(!)g 8! 2 :
Then,
(i) MINn and MAXn are rv's.
(ii) If X1; : : : ;Xn are independent, then
FMAXn(z) = P (MAXn � z) = P (Xi � z 8i = 1; : : : ; n) =nYi=1
FXi(z)
and
FMINn(z) = P (MINn � z) = 1� P (Xi > z 8i = 1; : : : ; n) = 1�nYi=1
(1� FXi(z)):
(iii) If fXigni=1 are iid rv's with common cdf FX , then
FMAXn(z) = FnX(z)
and
FMINn(z) = 1� (1� FX(z))n:
If FX is absolutely continuous with pdf fX , then the pdfs of MAXn and MINn are
fMAXn(z) = n � F n�1X (z) � fX(z)
and
fMINn(z) = n � (1� FX(z))n�1 � fX(z)
for all continuity points of FX .
83
Note:
Using Theorem 4.3.2, it is easy to derive the joint cdf and pdf of MAXn and MINn for iid rv's
fX1; : : : ;Xng. For example, if the Xi's are iid with cdf FX and pdf fX , then the joint pdf ofMAXn
and MINn is
fMAXn;MINn(x; y) =
(0; x � y
n(n� 1) � (FX (x)� FX(y))n�2 � fX(x)fX(y); x > y
However, note that MAXn and MINn are not independent. See Rohatgi, page 129, Corollary, for
more details.
84
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 28: Wednesday 11/3/1999
Scribe: J�urgen Symanzik, Rich Madsen
Note:
The previous transformations are special cases of the following Theorem:
Theorem 4.3.3:
If g : IRn ! IRm is a Borel{measurable function (i.e., 8B 2 Bm : g�1(B) 2 Bn) and if X =
(X1; : : : ; Xn) is an n{rv, then g(X) is an m{rv.
Proof:
If B 2 Bm, then f! : g(X(!)) 2 Bg = f! : X(!) 2 g�1(B)g 2 Bn.
Question: How do we handle more general transformations of X ?
Discrete Case:
Let X = (X1; : : : ;Xn) be a discrete n{rv and X � IRn be the countable support of X, i.e.,
P (X 2 X) = 1 and P (X = x) > 0 8x 2 X.
De�ne ui = gi(x1; : : : ; xn); i = 1; : : : ; n to be 1{to{1-mappings of X onto B. Let u = (u1; : : : ; un)0.
Then
P (U = u) = P (g1(X) = u1; : : : ; gn(X) = un) = P (X1 = h1(u); : : : ; Xn = hn(u)) 8u 2 B
where xi = hi(u); i = 1; : : : ; n, is the inverse transformation (and P (U = u) = 0 8u 62 B).
The joint marginal pmf of any subcollection of ui's is now obtained by summing over the other
remaining uj 's.
85
Example 4.3.4:
Let X;Y be iid � Bin(n; p); 0 < p < 1. Let U = XY+1 and V = Y + 1. Then X = UV and
Y = V � 1. So the joint pmf of U; V is
P (U = u; V = v) =
n
uv
!puv(1� p)n�uv
n
v � 1
!pv�1(1� p)n+1�v
=
n
uv
! n
v � 1
!puv+v�1(1� p)2n+1�uv�v
for v 2 f1; 2; : : : ; n+ 1g and uv 2 f0; 1; : : : ; ng.
Continuous Case:
Let X = (X1; : : : ;Xn) be a continuous n{rv with joint cdf FX and joint pdf fX .
Let
U =
0BB@
U1
...
Un
1CCA = g(X) =
0BB@
g1(X)...
gn(X)
1CCA ;
i.e., Ui = gi(X), be a mapping from IRn into IRn.
If B 2 Bn, then
P (U 2 B) = P (X 2 g�1(B)) =
R: : :R
g�1(B)fX(x)d(x) =
R: : :R
g�1(B)fX(x)
nYi=1
dxi
where g�1(B) = fx = (x1; : : : ; xn) 2 IRn : g(x) 2 Bg.
Suppose we de�ne B as the half{in�nite n{dimensional interval
Bu = f(u01; : : : ; u0n) : �1 < u0i < ui 8i = 1; : : : ; ng
for any u 2 IRn. Then the joint cdf of U is
G(u) = P (U 2 Bu) = P (g1(X) � u1; : : : ; gn(X) � un) =
R: : :R
g�1(Bu)fX(x)d(x):
If G happens to be absolutely continuous, the joint pdf of U will be given by fU (u) =@nG(u)
@u1@u2:::@un
at every continuity point of fU .
Under certain conditions, we can write fU in terms of the original pdf fX of X as stated in the
next Theorem:
86
Theorem 4.3.5: Multivariate Transformation
Let X = (X1; : : : ;Xn) be a continuous n{rv with joint pdf fX .
(i) Let
U =
0BB@
U1
...
Un
1CCA = g(X) =
0BB@
g1(X)...
gn(X)
1CCA ;
(i.e., Ui = gi(X)) be a 1{to{1{mapping from IRn into IR
n, i.e., there exist inverses hi,
i = 1; : : : ; n, such that xi = hi(u) = hi(u1; : : : ; un); i = 1; : : : ; n, over the range of the
transformation g.
(ii) Assume both g and h are continuous.
(iii) Assume partial derivatives @xi@uj
=@hi(u)@uj
; i; j = 1; : : : ; n, exist and are continuous.
(iv) Assume that the Jacobian of the inverse transformation
J =
����@(x1; : : : ; xn)@(u1; : : : ; un)
���� =��������
@x1@u1
: : :@x1@un
......
@xn@u1
: : :@xn@un
��������is di�erent from 0 for all u in the range of g.
Then the n{rv U = g(X) has a joint absolutely continuous cdf with corresponding joint pdf
fU(u) =j J j fX(h1(u); : : : ; hn(u)):
Proof:
Let u 2 IRn and
Bu = f(u01; : : : ; u0n) : �1 < u0i < ui 8i = 1; : : : ; ng:
Then,
GU(u) =
R: : :R
g�1(Bu)fX(x)d(x)
=
R: : :R
BufX(h1(u); : : : ; hn(u)) j J j d(u)
The result follows from di�erentiation of GU .
For additional steps of the proof see Rohatgi (page 135 and Theorem 17 on page 10) or a book on
multivariate calculus.
87
Theorem 4.3.6:
Let X = (X1; : : : ;Xn) be a continuous n{rv with joint pdf fX .
(i) Let
U =
0BB@
U1
...
Un
1CCA = g(X) =
0BB@
g1(X)...
gn(X)
1CCA ;
(i.e., Ui = gi(X)) be a mapping from IRn into IRn.
(ii) Let X = fx : fX(x) > 0g be the support of X .
(iii) Suppose that for each u 2 B = fu 2 IRn : u = g(x) for some x 2 Xg there is a �nite number
k = k(u) of inverses.
(iv) Suppose we can partition X into X0;X1; : : : ;Xk s.t.
(a) P (X 2 X0) = 0.
(b) U = g(X) is a 1{to{1{mapping from Xl onto B for all l = 1; : : : ; k, with inverse trans-
formation hl(u) =
0BB@
hl1(u)...
hln(u)
1CCA ; u 2 B, i.e., for each u 2 B, hl(u) is the unique x 2 Xl
such that u = g(x).
(v) Assume partial derivatives @xi@uj
=@hli(u)@uj
; l = 1; : : : ; k; i; j = 1; : : : ; n, exist and are continuous.
(vi) Assume the Jacobian of each of the inverse transformations
Jl =
��������
@x1@u1
: : :@x1@un
......
@xn@u1
: : :@xn@un
��������=
��������
@hl1@u1
: : :@hl1@un
......
@hln@u1
: : :@hln@un
��������; l = 1; : : : ; k;
is di�erent from 0 for all u in the range of g.
Then the joint pdf of U is given by
fU (u) =kXl=1
j Jl j fX(hl1(u); : : : ; hln(u)):
88
Example 4.3.7:
Let X;Y be iid � N(0; 1). De�ne
U = g1(X;Y ) =
(XY ; Y 6= 0
0; Y = 0
and
V = g2(X;Y ) =j Y j :
X = IR2, but U; V are not 1{to{1 mappings since (U; V )(x; y) = (U; V )(�x;�y), i.e., conditions do
not apply for the use of Theorem 4.3.5. Let
X0 = f(x; y) : y = 0g
X1 = f(x; y) : y > 0g
X2 = f(x; y) : y < 0g
Then P ((X;Y ) 2 X0) = 0.
Let B = f(u; v) : v > 0g = g(X1) = g(X2).
Inverses:
B ! X1 : x = h11(u; v) = uv
y = h12(u; v) = v
B ! X2 : x = h21(u; v) = �uv
y = h22(u; v) = �v
J1 =
����� v u
0 1
�����) jJ1j =j v j
J2 =
����� �v �u0 �1
�����) jJ2j =j v j
fX;Y (x; y) =1
2�e�x2=2
e�y2=2
fU;V (u; v) = j v j 1
2�e�(uv)2=2
e�v2=2+ j v j 1
2�e�(�uv)2=2
e�(�v)2=2
=v
�e�(u2+1)v2
2 ; �1 < u <1; 0 < v <1
Marginal:
fU (u) =
Z 1
0
v
�e�(u2+1)v2
2 dv j z =(u2 + 1)v2
2;
dz
dv= (u2 + 1)v
=
Z 1
0
1
�(u2 + 1)e�zdz
89
=1
�(u2 + 1)(�e�z)
����1
0
=1
�(1 + u2); �1 < u <1
Thus, the quotient of two iid N(0; 1) rv's is a rv that has a Cauchy distribution.
90
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 29: Friday 11/5/1999
Scribe: J�urgen Symanzik, Hanadi B. Eltahir
4.4 Order Statistics
De�nition 4.4.1:
Let (X1; : : : ;Xn) be an n{rv. The kth order statistic X(k) is the kth smallest of the X 0
is, i.e.,
X(1) = minfX1; : : : ;Xng, X(2) = minffX1; : : : ;Xng � X(1)g, : : :, X(n) = maxfX1; : : : ;Xng. It is
X(1) � X(2) � : : : � X(n) and fX(1);X(2); : : : ;X(n)g is the set of order statistics for (X1; : : : ; Xn).
Note:
As shown in Theorem 4.3.2, X(1) and X(n) are rv's. This result will be extended in the following
Theorem:
Theorem 4.4.2:
Let (X1; : : : ;Xn) be an n{rv. Then the kth order statistic X(k), k = 1; : : : ; n, is also an rv.
Theorem 4.4.3:
Let X1; : : : ; Xn be continuous iid rv's with pdf fX . The joint pdf of X(1); : : : ;X(n) is
fX(1);:::;X(n)(x1; : : : ; xn) =
8>><>>:
n!nYi=1
fX(xi); x1 � x2 � : : : � xn
0; otherwise
Proof:
For the case n = 3, look at the following scenario how X1;X2, and X3 can be possibly ordered to
yield X(1) < X(2) < X(3). Columns represent X(1);X(2), and X(3). Rows represent X1;X2, and X3:
X1 < X2 < X3 :
��������1 0 0
0 1 0
0 0 1
��������
X1 < X3 < X2 :
��������1 0 0
0 0 1
0 1 0
��������91
X2 < X1 < X3 :
��������0 1 0
1 0 0
0 0 1
��������
X2 < X3 < X1 :
��������0 0 1
1 0 0
0 1 0
��������
X3 < X1 < X2 :
��������0 1 0
0 0 1
1 0 0
��������
X3 < X2 < X1 :
��������0 0 1
0 1 0
1 0 0
��������For n = 3, there are 3! = 6 possible arrangements. In general, there are n! arrangements of
X1; : : : ;Xn for each (X(1); : : : ;X(n)). This mapping is not 1{to{1. For each mapping, we have a
n � n matrix J that results from an n � n identity matrix through the rearrangement or rows.
Therefore, j J j= 1. By Theorem 4.3.6, we get
fX(1);:::;X(n)(x(1); : : : ; x(n)) = n!fX1;:::;Xn(x(k1); x(k2); : : : ; x(kn))
= n!nYi=1
fXi(x(ki))
= n!nYi=1
fX(xi)
Theorem 4.4.4: Let X1; : : : ;Xn be continuous iid rv's with pdf fX and cdf FX . Then the following
holds:
(i) The marginal pdf of X(k), k = 1; : : : ; n, is
fX(k)(x) =
n!
(k � 1)!(n � k)!(FX(x))
k�1(1� FX(x))n�k
fX(x):
(ii) The joint pdf of X(j) and X(k), 1 � j < k � n, is
fX(j);X(k)(xj ; xk) =
n!
(j � 1)!(k � j � 1)!(n� k)!�
(FX(xj))j�1(FX(xk)� FX(xj))
k�j�1(1� FX(xk))n�k
fX(xj)fX(xk)
if xj < xk and 0 otherwise.
92
4.5 Multivariate Expectation
In this section, we assume that X = (X1; : : : ;Xn) is an n{rv and g : IRn ! IRn is a Borel{
measurable function.
De�nition 4.5.1:
If n = 1, i.e., g is univariate, we de�ne the following:
(i) Let X be discrete with joint pmf pi1;:::;in = P (X1 = xi1 ; : : : ; Xn = xin). IfXi1;:::;in
pi1;:::;in � j g(xi1 ; : : : ; xin) j< 1, we de�ne E(g(X)) =X
i1;:::;in
pi1;:::;in � g(xi1 ; : : : ; xin) and
this value exists.
(ii) Let X be continuous with joint pdf fX(x). If
ZIRn
j g(x) j fX(x) dx < 1, we de�ne
E(g(X)) =
ZIRn
g(x)fX(x)dx and this value exists.
Note:
The above can be extended to vector{valued functions g (n > 1) in the obvious way. For example,
if g is the identity mapping from IRn ! IR
n, then
E(X) =
0BB@
E(X1)...
E(Xn)
1CCA =
0BB@
�1
...
�n
1CCA
provided that E(j Xi j) <1 8i = 1; : : : ; n.
Similarly, provided that all expectations exist, we get for the variance{covariance matrix:
V ar(X) = �X = E((X �E(X)) (X �E(X))0)
with (i; j)th component
E((Xi �E(Xi)) (Xj �E(Xj))) = Cov(Xi; Xj)
and with (i; i)th component
E((Xi �E(Xi)) (Xi �E(Xi))) = V ar(Xi) = �2i :
Joint higher{order moments can be de�ned similarly when needed.
93
Note:
We are often interested in (weighted) sums of rv's or products of rv's and their expectations. This
will be addressed in the next two Theorems:
Theorem 4.5.2:
Let Xi; i = 1; : : : ; n, be rv's such that E(j Xi j) <1. Let a1; : : : ; an 2 IR and de�ne S =nXi=1
aiXi.
Then it holds that E(j S j) <1 and
E(S) =nXi=1
aiE(Xi):
Proof:
Continuous case only:
E(j S j) =
ZIRn
jnXi=1
aixi j fX(x)dx
�ZIRn
nXi=1
j ai j � j xi j fX(x)dx
=nXi=1
j ai jZIRj xi j
�ZIRn�1
fX(x)dx1 : : : dxi�1dxi+1 : : : dxn
�dxi
=nXi=1
j ai jZIRj xi j fXi
(xi)dxi
=nXi=1
j ai j E(j Xi j)
< 1
It follows that E(S) =nXi=1
aiE(Xi) by the same argument without using the absolute values j j.
Note:
If Xi; i = 1; : : : ; n, are iid with E(Xi) = �, then
E(X) = E(1
n
nXi=1
Xi) =nXi=1
1
nE(Xi) = �:
94
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 30: Monday 11/8/1999
Scribe: J�urgen Symanzik, Bill Morphet
Theorem 4.5.3:
Let Xi; i = 1; : : : ; n, be independent rv's such that E(j Xi j) < 1. Let gi; i = 1; : : : ; n, be Borel{
measurable functions. Then
E(nYi=1
gi(Xi)) =nYi=1
E(gi(Xi))
if all expectations exist.
Proof:
By Theorem 4.2.5, fX(x) =nYi=1
fXi(xi), and by Theorem 4.2.7, gi(Xi); i = 1; : : : ; n, are also inde-
pendent. Therefore,
E(nYi=1
gi(xi)) =
ZIRn
nYi=1
gi(xi)fX(x)dx
Th:4:2:5=
ZIRn
nYi=1
(gi(xi)fXi(xi)dxi)
=
ZIR: : :
ZIR
nYi=1
gi(xi)nYi=1
fXi(xi)
nYi=1
dxi
Th: 4:2:7=
ZIRg1(x1)fX1
(x1)dx1
ZIRg2(x2)fX2
(x2)dx2 : : :
ZIRgn(xn)fXn(xn)dxn
=nYi=1
ZIRgi(xi)fXi
(xi)dxi
=nYi=1
E(gi(Xi))
Corollary 4.5.4:
If X;Y are independent, then Cov(X;Y ) = 0.
Theorem 4.5.5:
Two rv's X;Y are independent i� for all pairs of Borel{measurable functions g1 and g2 it holds
that E(g1(X) � g2(Y )) = E(g1(X)) �E(g2(Y )) if all expectations exist.Proof:
\=)": It follows from Theorem 4.5.3 and the independence of X and Y that
E(g1(X)g2(Y )) = E(g1(X)) �E(g2(Y )):
95
\(=": From Theorem 4.2.6, we know that X and Y are independent i� P (X 2 A1; Y 2 A2) =
P (X 2 A1) P (Y 2 A2) 8 Borel sets A1 and A2.
How do we relate Theorem 4.2.6 to g1 and g2? Let us de�ne two Borel{measurable functions g1
and g2 as:
g1(x) = IA1(x) =
(1; x 2 A1
0; otherwise
g2(y) = IA2(y) =
(1; y 2 A2
0; otherwise
Then,
E(g1(X)) = 0 � P (X 2 Ac1) + 1 � P (X 2 A1) = P (X 2 A1);
E(g2(Y )) = 0 � P (Y 2 Ac2) + 1 � P (Y 2 A2) = P (Y 2 A2)
and
E(g1(X) � g2(Y )) = P (X 2 A1; Y 2 A2):
=) P (X 2 A1; Y 2 A2) = E(g1(X) � g2(Y ))given= E(g1(X)) � E(g2(Y )) = P (X 2 A1)P (Y 2 A2)
=) P (X 2 A1; Y 2 A2) = P (X 2 A1) P (Y 2 A2)
=) X; Y independent by Theorem 4.2.6.
De�nition 4.5.6:
The (ith1 ; ith2 ; : : : ; i
thn ) multi{way moment of X = (X1; : : : ; Xn) is de�ned as
mi1i2:::in = E(Xi11 X
i22 : : : X
inn )
if it exists.
The (ith1 ; ith2 ; : : : ; i
thn ) multi{way central moment of X = (X1; : : : ;Xn) is de�ned as
�i1i2:::in = E(nYj=1
(Xj �E(Xj))ij )
if it exists.
Note:
If we set ir = is = 1 and ij = 0 8j 6= r; s in De�nition 4.5.6, we get
�0 : : : 0 1 0 : : : 0 1 0 : : : 0
"r
"s
= �rs = Cov(Xr;Xs):
96
Theorem 4.5.7: Cauchy{Schwarz{Inequality
Let X;Y be 2 rv's with �nite variance. Then it holds:
(i) Cov(X;Y ) exists.
(ii) (E(XY ))2 � E(X2)E(Y 2).
(iii) (E(XY ))2 = E(X2)E(Y 2) i� there exists an (�; �) 2 IR2 � f(0; 0)g such that
P (�X + �Y = 0) = 1.
Proof:
Assumptions: V ar(X); V ar(Y ) <1. Then also E(X2); E(X); E(Y 2); E(Y ) <1.
Result used in proof:
0 � (a� b)2 = a2 � 2ab+ b
2 =) ab � a2+b2
2
0 � (a+ b)2 = a2 + 2ab+ b
2 =) �ab � a2+b2
2
=)j ab j � a2+b2
2 8 a; b 2 IR (�)
(i)
E(j XY j) =
ZIR2
j xy j fX;Y (x; y)dx dy
(�)�
ZIR2
x2 + y
2
2fX;Y (x; y)dx dy
=
ZIR2
x2
2fX;Y (x; y)dx dy +
ZIR2
y2
2fX;Y (x; y)dy dx
=
ZIR
x2
2fX(x)dx+
ZIR
y2
2fY (y)dy
=E(X2) + E(Y 2)
2
< 1
=) E(XY ) exists
=) Cov(X;Y ) = E(XY )�E(X)E(Y ) exists
(ii) 0 � E((�X + �Y )2) = �2E(X2) + 2��E(XY ) + �
2E(Y 2) 8 �; � 2 IR (A)
If E(X2) = 0, then X has a degenerate 1{point Dirac distribution and the inequality trivially
is true. Therefore, we can assume that E(X2) > 0. As (A) is true for all �; � 2 IR, we can
choose � =�E(XY )E(X2)
; � = 1.
97
=) (E(XY ))2
E(X2)� 2
(E(XY ))2
E(X2)+E(Y 2) � 0
=) �(E(XY ))2 +E(Y 2)E(X2) � 0
=) (E(XY ))2 � E(X2) E(Y 2)
(iii) When are the left and right sides of the inequality in (ii) equal?
Assume that E(X2) > 0. (E(XY ))2 = E(X2)E(Y 2) holds i� E((�X + �Y )2) = 0. This can
only happen if P (�X + �Y = 0) = 1.
Otherwise, if P (�X + �Y = 0) = P (Y = ���X) = 1 for some (�; �) 2 IR
2 � f(0; 0)g, i.e., Yis linearly dependent on X with probability 1, this implies:
(E(XY ))2 = (E(X � ��X�
))2 = (�
�)2(E(X2))2 = E(X2)(
�
�)2E(X2) = E(X2)E(Y 2)
4.6 Multivariate Generating Functions
De�nition 4.6.1:
Let X = (X1; : : : ;Xn) be an n{rv. We de�ne the multivariate moment generating function
(mmgf) of X as
MX(t) = E(et0X) = E(exp
nXi=1
tiXi
!)
if this expectation exists for j t j=
vuut nXi=1
t2i < h for some h > 0.
De�nition 4.6.2:
Let X = (X1; : : : ; Xn) be an n{rv. We de�ne the n{dimensional characteristic function
�X : IRn ! CI of X as
�X(t) = E(eit0X) = E(exp
0@i nX
j=1
tjXj
1A):
Note:
(i) �X(t) exists for any real{valued n{rv.
(ii) If MX(t) exists, then �X(t) =MX(it).
98
Theorem 4.6.3:
(i) If MX(t) exists, it is unique and uniquely determines the joint distribution of X . �X(t) is
also unique and uniquely determines the joint distribution of X.
(ii) MX(t) (if it exists) and �X(t) uniquely determine all marginal distributions of X, i.e.,
MXi(ti) =MX(0; ti; 0) and and �Xi
(ti) = �X(0; ti; 0).
(iii) Joint moments of all orders (if they exist) can be obtained as
mi1:::in =@i1+i2+:::+in
@ti11 @t
i22 : : : @t
inn
MX(t)
�����t=0
= E(Xi11 X
i22 : : : X
inn )
if the mmgf exists and
mi1:::in =1
ii1+i2+:::+in
@i1+i2+:::+in
@ti11 @t
i22 : : : @t
inn
�X(0) = E(Xi11 X
i22 : : : X
inn ):
(iv) X1; : : : ;Xn are independent rv's i�
MX(t1; : : : ; tn) =MX(t1; 0) �MX(0; t2; 0) � : : : �MX(0; tn) 8t1; : : : ; tn 2 IR;
given that MX(t) exists.
Similarly, X1; : : : ;Xn are independent rv's i�
�X(t1; : : : ; tn) = �X(t1; 0) � �X(0; t2; 0) � : : : � �X(0; tn) 8t1; : : : ; tn 2 IR:
Proof:
Rohatgi, page 162: Theorem 7, Corollary, Theorem 8, and Theorem 9 (for mmgf and the case
n = 2).
Theorem 4.6.4:
Let X1; : : : ; Xn be independent rv's.
(i) If mgf's MX1(t); : : : ;MXn(t) exist, then the mgf of Y =
nXi=1
aiXi is
MY (t) =nYi=1
MXi(ait)
on the common interval where all individual mgf's exist.
(ii) The characteristic function of Y =nXj=1
ajXj is
�Y (t) =nYj=1
�Xj(ajt)
99
(iii) If mgf's MX1(t); : : : ;MXn(t) exist, then the mmgf of X is
MX(t) =nYi=1
MXi(ti)
on the common interval where all individual mgf's exist.
(iv) The n{dimensional characteristic function of X is
�X(t) =nYj=1
�Xj(tj):
Proof:
Homework (parts (ii) and (iv) only)
100
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 31: Wednesday 11/10/1999
Scribe: J�urgen Symanzik, Rich Madsen
Theorem 4.6.5:
LetX1; : : : ; Xn be independent discrete rv's on the non{negative integers with pgf'sGX1(s); : : : ; GXn(s).
The pgf of Y =nXi=1
Xi is
GY (s) =nYi=1
GXi(s):
Proof:
Version 1:
GXi(s) = E(sXi)
GY (s) = E(sY )
= E(sPn
i=1Xi)
indep:=
nYi=1
E(sXi)
=nYi=1
GXi(s)
Version 2: (case n = 2 only)
GY (s) = P (Y = 0) + P (Y = 1)s+ P (Y = 2)s2 + : : :
= P (X1 = 0; X2 = 0) +
(P (X1 = 1;X2 = 0) + P (X1 = 0;X2 = 1)) s+
(P (X1 = 2;X2 = 0) + P (X1 = 1;X2 = 1) + P (X1 = 0;X2 = 2)) s2 + : : :
indep:= P (X1 = 0)P (X2 = 0) +
(P (X1 = 1)P (X2 = 0) + P (X1 = 0)P (X2 = 1)) s+
(P (X1 = 2)P (X2 = 0) + P (X1 = 1)P (X2 = 1) + P (X1 = 0)P (X2 = 2)) s2 + : : :
=�P (X1 = 0) + P (X1 = 1)s+ P (X1 = 2)s2 + : : :
���
P (X2 = 0) + P (X2 = 1)s+ P (X2 = 2)s2 + : : :
�= GX1
(s) �GX2(s)
101
A generalized proof for n � 3 needs to be done by induction on n.
Theorem 4.6.6:
Let X1; : : : ;XN be iid discrete rv's on the non{negative integers with common pgf GX(s). Let N
be a discrete rv on the non{negative integers with pgf GN (s). Let N be independent of the Xi's.
De�ne SN =NXi=1
Xi. The pgf of SN is
GSN (s) = GN (GX(s)):
Proof:
P (SN = k) =1Xn=0
P (SN = kjN = n) � P (N = n)
=) GSN (s) =1Xk=0
1Xn=0
P (SN = kjN = n) � P (N = n) � sk
=1Xn=0
P (N = n)1Xk=0
P (SN = kjN = n) � sk
=1Xn=0
P (N = n)1Xk=0
P (Sn = k) � sk
=1Xn=0
P (N = n)1Xk=0
P (nXi=1
Xi = k) � sk
Th:4:6:5=
1Xn=0
P (N = n)nYi=1
GXi(s)
iid=
1Xn=0
P (N = n) � (GX(s))n
= GN (GX(s))
Example 4.6.7:
Starting with a single cell at time 0, after one time unit there is probability p that the cell will have
split (2 cells), probability q that it will survive without splitting (1 cell), and probability r that it
will have died (0 cells). It holds that p; q; r � 0 and p + q + r = 1. Any surviving cells have the
same probabilities of splitting or dying. What is the pgf for the # of cells at time 2?
GX(s) = GN (s) = ps2 + qs+ r
GSN (s) = p(ps2 + ps+ r)2 + q(ps2 + ps+ r) + r
102
Theorem 4.6.8:
Let X1; : : : ; XN be iid rv's with common mgf MX(t). Let N be a discrete rv on the non{negative
integers with mgf MN (t). Let N be independent of the Xi's. De�ne SN =NXi=1
Xi. The mgf of SN
is
MSN (t) =MN (lnMX(t)):
Proof:
Consider the case that the Xi's are non{negative integers:
We know that
GX(S) = E(SX) = E(eln SX
) = E(e(ln S)�X) =MX(lnS)
=)MX(S) = GX(eS)
=)MSN (t) = GSN (et)
Th:4:6:6= GN (GX(e
t)) = GN (MX(t)) =MN (lnMX(t))
In the general case, i.e., if the X 0is are not non{negative integers, we need results from Section 4.7
(conditional expectation) to proof this Theorem.
103
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 32: Friday 11/12/1999
Scribe: J�urgen Symanzik, Hanadi B. Eltahir
4.7 Conditional Expectation
In Section 4.1, we established that the conditional pmf of X given Y = yj (for PY (yj) > 0) is a
pmf. For continuous rv's X and Y , when fY (y) > 0, fXjY (x j y) =fX;Y (x;y)fY (y)
, and fX;Y and fY are
continuous, then fXjY (x j y) is a pdf and it is the conditional pdf of X given Y = y.
De�nition 4.7.1:
Let X;Y be rv's on (; L; P ). Let h be a Borel{measurable function. Assume that E(h(X)) exists.
Then the conditional expectation of h(X) given Y , i.e., E(h(X) j Y ), is a rv that takes the
value E(h(X) j y). It is de�ned as
E(h(X) j y) =
8>>>><>>>>:
Xx2X
h(x)P (X = x j Y = y); if (X;Y ) is discrete and P (Y = y) > 0
Z 1
�1h(x)fXjY (x j y)dx; if (X;Y ) is continuous and fY (y) > 0
Note:
(i) The rv E(h(X) j Y ) = g(Y ) is a function of Y as a rv.
(ii) The usual properties of expectations apply to the conditional expectation:
(a) E(c j Y ) = c 8c 2 IR.
(b) E(aX + b j Y ) = aE(X j Y ) + b 8a; b 2 IR.
(c) If g1; g2 are Borel{measurable functions and if E(g1(X)); E(g2(X)) exist, then
E(a1g1(X) + a2g2(X) j Y ) = a1E(g1(X) j Y ) + a2E(g2(X) j Y ) 8a1; a2 2 IR.
(d) If X � 0 then E(X j Y ) � 0.
(e) If X1 � X2 then E(X1 j Y ) � E(X2 j Y ).
(iii) Moments are de�ned in the usual way. If E(j X jr) < 1, then E(Xr j Y ) exists and is the
rth conditional moment of X given Y .
104
Example 4.7.2:
Recall Example 4.1.12:
fX;Y (x; y) =
(2; 0 < x < y < 1
0; otherwise
The conditional pdf's fY jX(y j x) and fXjY (x j y) have been calculated as:
fY jX(y j x) =1
1� xfor x < y < 1 (where 0 < x < 1)
and
fXjY (x j y) =1
yfor 0 < x < y (where 0 < y < 1).
So,
E(X j y) =Z y
0
x
ydx =
y
2
and
E(Y j x) =Z 1
x
1
1� xy dy =
1
1� x
y2
2
�����1
x
=1
2
1� x2
1� x=
1 + x
2:
Therefore, we get the rv's E(X j Y ) = Y2 and E(Y j X) = 1+X
2 .
Theorem 4.7.3:
If E(h(X)) exists, then
EY (EXjY (h(X) j Y )) = E(h(X)):
Proof:
Continuous case only:
EY (E(h(X) j Y )) =
Z 1
�1E(h(x) j y)fY (y)dy
=
Z 1
�1
Z 1
�1h(x)fXjY (x j y)fY (y)dxdy
=
Z 1
�1h(x)
Z 1
�1fX;Y (x; y)dydx
=
Z 1
�1h(x)fX(x)dx
= E(h(X))
Theorem 4.7.4:
If E(X2) exists, then
V arY (E(X j Y )) +EY (V ar(X j Y )) = V ar(X):
105
Proof:
V arY (E(X j Y )) +EY (V ar(X j Y )) = EY ((E(X j Y ))2)� (EY (E(X j Y )))2
+EY (E(X2 j Y )� (E(X j Y ))2)
= EY ((E(X j Y ))2)� (E(X))2 +E(X2)�EY ((E(X j Y ))2)
= E(X2)� (E(X))2
= V ar(X)
Note:
If E(X2) exists, then V ar(X) � V arY (E(X j Y )). V ar(X) = V arY (E(X j Y )) i� X = g(Y ). The
inequality directly follows from Theorem 4.7.4.
For equality, it is necessary that EY (V ar(X j Y )) = EY ((X � E(X j Y ))2 j Y ) = 0 which holds if
X = E(X j Y ) = g(Y ).
If X;Y are independent, FXjY (x j y) = FX(x) 8x. Thus, if E(h(X)) exists, then E(h(X) j Y ) =E(h(X)).
4.8 Inequalities and Identities
Lemma 4.8.1:
Let a; b be positive numbers and p; q > 1 such that 1p +
1q = 1 (i.e., pq = p+ q and q = p
p�1). Then
it holds that1
pap +
1
qbq � ab
with equality i� ap = b
q.
Proof:
Fix b. Let
g(a) =1
pap +
1
qbq � ab
=) g0(a) = a
p�1 � b!= 0
=) b = ap�1
=) bq = a
(p�1)q = ap
g00(a) = (p� 1)ap�2 > 0
106
Since g00(a) > 0, this is really a minimum. The minimum value is obtained for b = ap�1 and it is
1
pap +
1
q(ap�1)q � aa
p�1 =1
pap +
1
qap � a
p = ap(1
p+1
q� 1) = 0:
Since g00(a) > 0, the minimum is unique and g(a) � 0. Therefore g(a) + ab = 1pa
p + 1q b
q � ab.
Theorem 4.8.2: H�olders Inequality
Let X;Y be 2 rv's. Let p; q > 1 such that 1p +
1q = 1 (i.e., pq = p+ q and q = p
p�1). Then it holds
that
E(j XY j) � (E(j X jp))1p (E(j Y jq))
1q :
Proof:
In Lemma 4.8.1, let a =jXj
(E(jXjp))1pand b =
jY j(E(jY jq))
1q.
Lemma 4:8:1=) 1
pjXjp
E(jXjp) +1q
jY jqE(jY jq) �
jXY j(E(jXjp))
1p (E(jY jq))
1q
Taking expectations on both sides of this inequality, we get
1 = 1p +
1q �
E(jXY j)(E(jXjp))
1p (E(jY jq))
1q
The result follows immediately when multiplying both sides with (E(j X jp))1p (E(j Y jq))
1q .
Note:
Note that Theorem 4.5.7 (ii) (Cauchy{Schwarz{Inequality) is a special case of Theorem 4.8.2 with
p = q = 2.
107
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 33: Monday 11/15/1999
Scribe: J�urgen Symanzik, Bill Morphet
Theorem 4.8.3: Minkowski's Inequality
Let X;Y be 2 rv's. Then it holds for 1 � p <1 that
(E(j X + Y jp))1p � (E(j X jp))
1p + (E(j Y jp))
1p :
Proof:
E(j X + Y jp) = E(j X + Y j � j X + Y jp�1)
� E((j X j + j Y j)� j X + Y jp�1)
= E(j X j � j X + Y jp�1) + E(j Y j � j X + Y jp�1)Th:4:8:2� ( E(j X jp) )
1p � ( E( (j X + Y jp�1)q ) )
1q
+ ( E(j Y jp) )1p � ( E( (j X + Y jp�1)q ) )
1q (A)
(�)=
�( E(j X jp) )
1p + ( E(j Y jp) )
1p
�� ( E(j X + Y jp ) )
1q
Divide the left and right side of this inequality by (E(j X + Y jp))1q .
The result on the left side is (E(j X + Y jp))1�1q = (E(j X + Y jp))
1p , and the result on the right
side is ( E(j X jp) )1p + ( E(j Y jp) )
1p . Therefore, Theorem 4.8.3 holds.
In (A), we de�ne q = pp�1 . Therefore,
1p +
1q = 1 and (p� 1)q = p.
(�) holds since 1p+ 1
q= 1, a condition to meet H�older's Inequality.
De�nition 4.8.4:
A function g(x) is convex if
g(�x+ (1� �)y) � �g(x) + (1� �)g(y) 8x; y 2 IR 80 < � < 1:
Note:
(i) Geometrically, a convex function falls above all of its tangent lines. Also, a connecting line
between any pairs of points (x; g(x)) and (y; g(y)) in the 2{dimensional plane always falls
above the curve.
108
(ii) A function g(x) is concave i� �g(x) is convex.
Theorem 4.8.5: Jensen's Inequality
Let X be a rv. If g(x) is a convex function, then
E(g(X)) � g(E(X))
given that both expectations exist.
Proof:
Construct a tangent line l(x) to g(x) at the (constant) point x0 = E(X):
l(x) = ax+ b for some a; b 2 IR
The function g(x) is a convex function and falls above the tangent line l(x)
=) g(x) � ax+ b 8x 2 IR
=) E(g(X)) � E(aX + b) = aE(X) + b = l(E(X))tangent point
= g(E(X))
Therefore, Theorem 4.8.5 holds.
Note:
Typical convex functions g are:
(i) g1(x) =j x j ) E(j X j) �j E(X) j.
(ii) g2(x) = x2 ) E(X2) � (E(X))2 ) V ar(X) � 0.
(iii) g3(x) =1xp for x > 0; p > 0 ) E( 1
Xp ) � 1(E(X))p ; for p = 1: E( 1
X ) �1
E(X)
(iv) Other convex functions are xp for x > 0; p � 1; �x for � > 1; � ln(x) for x > 0; etc.
(v) Recall that if g is convex and di�erentiable, then g00(x) � 0 8x.
(vi) If the function g is concave, the direction of the inequality in Jensen's Inequality is reversed,
i.e., E(g(X)) � g(E(X)).
Example 4.8.6:
Given the real numbers a1; a2; : : : ; an > 0, we de�ne
arithmetic mean : aA =1
n(a1 + a2 + : : : + an) =
1
n
nXi=1
ai
109
geometric mean : aG = (a1 � a2 � : : : � an)1n =
nYi=1
ai
! 1n
harmonic mean : aH =1
1n
�1a1+ 1
a2+ : : :+ 1
an
� =1
1n
nXi=1
1
ai
Let X be a rv that takes values a1; a2; : : : ; an > 0 with probability 1neach.
(i) aA � aG:
ln(aA) = ln
1
n
nXi=1
ai
!
= ln(E(X))
ln concave� E(ln(X))
=nXi=1
1
nln(ai)
=1
n
nXi=1
ln(ai)
=1
nln(
nYi=1
ai)
= ln((nYi=1
ai)1n )
= ln(aG)
Taking the anti{log of both sides gives aA � aG.
(ii) aA � aH :
1
aA=
1
1n
nXi=1
ai
=1
E(X)
1=X convex
� E(1
X)
=nXi=1
1
n
1
ai
=1
n
�1
a1+
1
a2+ : : :
1
an
�
=1
aH
Inverting both sides gives aA � aH .
110
(iii) aG � aH :
� ln(aH) = ln(a�1H )
= ln(1
aH)
= ln
�1
n(1
a1+
1
a2+ : : :+
1
an)
�
= ln(E(1
X))
ln concave� E(ln(
1
X))
=nXi=1
1
nln(
1
ai)
=1
n
nXi=1
ln(1
ai)
=1
n
nXi=1
� lnai
= � 1
nln(
nYi=1
ai)
= � ln(nYi=1
ai)1n
= � lnaG
Multiplying both sides with �1 gives lnaH � lnaG. Then taking the anti{log of both sides
gives aH � aG.
In summary, aH � aG � aA. Note that it would have been su�cient to prove steps (i) and (iii)
only to establish this result. However, step (ii) has been included to provide another example how
to apply Theorem 4.8.5.
Theorem 4.8.7: Covariance Inequality
Let X be a rv with �nite mean �.
(i) If g(x) is non{decreasing, then
E(g(X)(X � �)) � 0
if this expectation exists.
(ii) If g(x) is non{decreasing and h(x) is non{increasing, then
E(g(X)h(X)) � E(g(X))E(h(X))
if all expectations exist.
111
(iii) If g(x) and h(x) are both non{decreasing or if g(x) and h(x) are both non{increasing, then
E(g(X)h(X)) � E(g(X))E(h(X))
if all expectations exist.
Proof:
Homework
112
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 34: Wednesday 11/17/1999
Scribe: J�urgen Symanzik, Rich Madsen
5 Particular Distributions
5.1 Multivariate Normal Distributions
De�nition 5.1.1:
A rv X has a (univariate) Normal distribution, i.e., X � N(�; �2) with � 2 IR and � > 0, i�
it has the pdf
fX(x) =1p2��2
e� 1
2�2(x��)2
:
X has a standard Normal distribution i� � = 0 and �2 = 1, i.e., X � N(0; 1).
Note:
If X � N(�; �2), then E(X) = � and V ar(X) = �2. If X1 � N(�1; �
21); X2 � N(�2; �
22) and
c1; c2 2 IR, then Y = c1X1 + c2X2 � N(c1�1 + c2�2; c21�
21 + c
22�
22).
De�nition 5.1.2:
A 2{rv (X;Y ) has a bivariate Normal distribution i� there exist constants a11; a12; a21; a22; �1; �2 2IR and iid N(0; 1) rv's Z1 and Z2 such that
X = �1 + a11Z1 + a12Z2; Y = �2 + a21Z1 + a22Z2:
If we de�ne
A =
a11 a12
a21 a22
!; � =
�1
�2
!; X =
X
Y
!; Z =
Z1
Z2
!;
then we can write
X = AZ + �:
Note:
E(X) = �1 + a11E(Z1) + a12E(Z2) = �1 and E(Y ) = �2 + a21E(Z1) + a22E(Z2) = �2. The
marginal distributions are X � N(�1; a211 + a
212) and Y � N(�2; a
221 + a
222). Thus, X and Y have
(univariate) Normal marginal densities or degenerate marginal densities (which correspond to Dirac
distributions) if ai1 = ai2 = 0.
113
Theorem 5.1.3:
De�ne g : IR2 ! IR2 as g(x) = Cx+ d. If X is a bivariate Normal rv, then g(X) also is a bivariate
Normal rv.
Proof:
g(X) = CX + d
= C(AZ + �) + d
= (CA)| {z }another matrix
Z + (C�+ d)| {z }another vector
= ~AZ + ~� which represents another bivariate Normal distribution
Note:
��1�2 = Cov(X;Y ) = Cov(a11Z1 + a12Z2; a21Z1 + a22Z2)
= a11a21Cov(Z1; Z1) + (a11a22 + a12a21)Cov(Z1; Z2) + a12a22Cov(Z2; Z2)
= a11a21 + a12a22
since Z1; Z2 are iid N(0; 1) rv's.
De�nition 5.1.4:
The variance{covariance matrix of (X;Y ) is
� = AA0 =
a11 a12
a21 a22
! a11 a21
a12 a22
!=
a211 + a
212 a11a21 + a12a22
a11a21 + a12a22 a221 + a
222
!=
�21 ��1�2
��1�2 �22
!:
Theorem 5.1.5:
Assume that �1 > 0; �2 > 0 and j � j< 1. Then the joint pdf of X = (X;Y ) = AZ + � (as de�ned
in De�nition 5.1.2) is
fX(x) =1
2�pj � j
exp
��1
2(x� �)0��1(x� �)
�
=1
2��1�2p1� �2
exp
� 1
2(1 � �2)
�x� �1
�1
�2
� 2�
�x� �1
�1
��y � �2
�2
�+
�y � �2
�2
�2!!
Proof:
The mapping Z ! X is 1{to{1:
X = AZ + �
114
=) Z = A�1(X � �) (requirement is that A is invertible.)
J = jA�1j = 1
jAj
jAj =qjAj2 =
qjAj � jAT j =
qjAAT j =
qj�j =
q�21�
22 � �2�21�
22 = �1�2
q1� �2
We can use this result to get to the second line of the theorem:
fZ(z) =1p2�
e� z2
12
1p2�
e� z2
22
=1
2�e� 12(zT z)
As already stated, the mapping from Z to X is 1{to{1, so we can apply Theorem 4.3.5:
fX(x) =1
2�pj�j
exp(�1
2(x� �)T (A�1)TA�1| {z }
��1
(x� �))
(�)=
1
2�pj�j
exp(�1
2(x� �)T��1(x� �))
This proves the 1st line of the Theorem. Step (�) holds since
(A�1)TA�1 = (AT )�1A�1 = (AAT )�1 = ��1:
The second line of the Theorem is based on the following transformations:qj�j = �1�2
q1� �2
��1 =1
j�j �
�22 ���1�2
���1�2 �21
!
=
0@ 1
�21(1��2)��
�1�2(1��2)��
�1�2(1��2)1
�22(1��2)
1A
=) fX(x) =1
2��1�2p1� �2
exp
� 1
2(1� �2)
�x� �1
�1
�2
� 2�
�x� �1
�1
��y � �2
�2
�+
�y � �2
�2
�2!!
Note:
In the situation of Theorem 5.1.5, we say that (X;Y ) � N(�1; �2; �21 ; �
22 ; �).
Theorem 5.1.6:
If (X;Y ) has a non{degenerate N(�1; �2; �21 ; �
22 ; �) distribution, then the conditional distribution
of X given Y = y is
N(�1 + ��1
�2(y � �2); �
21(1� �
2)):
115
Proof:
Homework
Example 5.1.7:
Let rv's (X1; Y1) be N(0; 0; 1; 1; 0) with pdf f1(x; y) and (X2; Y2) be N(0; 0; 1; 1; �) with pdf f2(x; y).
Let (X;Y ) be the rv that corresponds to the pdf
fX;Y (x; y) =1
2f1(x; y) +
1
2f2(x; y):
(X;Y ) is a bivariate Normal rv i� � = 0. However, the marginal distributions of X and Y are
always N(0; 1) distributions. See also Rohatgi, page 229, Remark 2.
116
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 35: Friday 11/19/1999
Scribe: J�urgen Symanzik, Bill Morphet
Theorem 5.1.8:
The mgf MX(t) of a bivariate Normal rv X = (X;Y ) is
MX(t) =MX;Y (t1; t2) = exp(�0t+1
2t0�t) = exp
��1t1 + �2t2 +
1
2(�21t
21 + �
22t
22 + 2��1�2t1t2)
�:
Proof:
The mgf of a univariate Normal rv X � N(�; �2) will be used to develop the mgf of a bivariate
Normal rv X = (X;Y ):
MX(t) = E(exp(tX))
=
Z 1
�1exp(tx)
1p2��2
exp
�� 1
2�2(x� �)2
�dx
=
Z 1
�1
1p2��2
exp
�� 1
2�2[�2�2tx + (x� �)2]
�dx
=
Z 1
�1
1p2��2
exp
�� 1
2�2[�2�2tx + (x2 � 2�x + �
2)]
�dx
=
Z 1
�1
1p2��2
exp
�� 1
2�2[x2 � 2(� + �
2t)x + (�+ t�
2)2 � (�+ t�2)2 + �
2]
�dx
= exp
�� 1
2�2[�(�+ t�
2)2 + �2]
�Z 1
�1
1p2��2
exp
�� 1
2�2[x� (�+ t�
2)]2�
dx| {z }pdf of N(�+ t�
2; �
2), that integrates to 1
= exp
�� 1
2�2[��2 � 2�t�2 � t
2�4 + �
2]
�
= exp
�2�t�2 � t
2�4
�2�2
!
= exp
��t+
1
2�2t2
�
Bivariate Normal mgf:
MX;Y (t1; t2) =
Z 1
�1
Z 1
�1exp(t1x+ t2y) fX;Y (x; y) dx dy
117
=
Z 1
�1
Z 1
�1exp(t1x) exp(t2y) fX(x) fY jX(y j x) dy dx
=
Z 1
�1
�Z 1
�1exp(t2y) fY jX(y j x) dy
�exp(t1x) fX(x) dx
(A)=
Z 1
�1
Z 1
�1
exp(t2y)
�2
p1� �2
p2�
exp
�(y � �X)
2
2�22(1� �2)
!dy
!exp(t1x) fX(x) dx
j �X = �2 + ��2
�1(x� �1)
(B)=
Z 1
�1exp
��X t2 +
1
2�22(1� �
2)t22
�exp(t1x) fX(x) dx
=
Z 1
�1exp
�[�2 + �
�2
�1(x� �1)]t2 +
1
2�22(1� �
2)t22 + t1x
�fX(x) dx
=
Z 1
�1exp
��2t2 + �
�2
�1t2x� �
�2
�1�1t2 +
1
2�22(1� �
2)t22 + t1x
�fX(x) dx
= exp
�1
2�22(1� �
2)t22 + t2�2 � ��2
�1�1t2
�Z 1
�1exp
�(t1 + �
�2
�1t2)x
�fX(x) dx
(C)= exp
�1
2�22(1� �
2)t22 + t2�2 � ��2
�1�1t2
�� exp
��1(t1 + �
�2
�1t2) +
1
2�21(t1 + �
�2
�1t2)
2
�
= exp
�1
2�22t
22 �
1
2�2�22t
22 + �2t2 � �1�
�2
�1t2 + �1t1 + �1�
�2
�1t2 +
1
2�21t
21 + ��1�2t1t2 +
1
2�2�22t
22
�
= exp
�1t1 + �2t2 +
�21t
21 + �
22t
22 + 2��1�2t1t2
2
!
(A) follows from Theorem 5.1.6 since Y j X � N(�X ; �22(1 � �
2)). (B) follows when we apply our
calculations of the mgf of a N(�; �2) distribution to a N(�X ; �22(1 � �
2)) distribution. (C) holds
since the integral represents MX(t1 + ��2�1t2).
Corollary 5.1.9:
Let (X;Y ) be a bivariate Normal rv. X and Y are independent i� � = 0.
De�nition 5.1.10:
Let Z be a k{rv of k iid N(0; 1) rv's. Let A 2 IRk�k be a k � k matrix, and let � 2 IR
k be a
k{dimensional vector. Then X = AZ + � has a multivariate Normal distribution with mean
vector � and variance{covariance matrix � = AA0.
118
Note:
(i) If � is non{singular, X has the joint pdf
fX(x) =1
(2�)k=2(j � j)1=2exp
��1
2(x� �)0��1(x� �)
�:
(ii) If � is singular, then X � � takes values in a linear subspace of IRk with probability 1.
(iii) If � is non{singular, then X has mgf
MX(t) = exp(�0t+1
2t0�t):
Theorem 5.1.11:
The components X1; : : : ;Xk of a normally distributed k{rv X are independent i� Cov(Xi;Xj) = 0
8i; j = 1; : : : ; k; i 6= j.
Theorem 5.1.12:
Let X = (X1; : : : ;Xk)0. X has a k{dimensional Normal distribution i� every linear function of X ,
i.e., X 0t = t1X1 + t2X2 + : : :+ tkXk, has a univariate Normal distribution.
Proof:
The Note following De�nition 5.1.1 states that any linear function of two Normal rv's has a uni-
variate Normal distribution. By induction on k, we can show that every linear function of X, i.e.,
X0t, has a univariate Normal distribution.
Conversely, if X 0t has a univariate Normal distribution, we know from Theorem 5.1.8 that
MX0t(s) = exp
�E(X 0
t) � s+ 1
2V ar(X 0
t) � s2�
= exp
��0ts+
1
2t0�ts2
�
=)MX0t(1) = exp
��0t+
1
2t0�t�
= MX(t)
By uniqueness of the mgf and Note (iii) that follows De�nition 5.1.10, X has a multivariate Normal
distribution.
119
5.2 Exponential Familty of Distributions
De�nition 5.2.1:
Let # be an interval on the real line. Let ff(�; �) : � 2 #g be a family of pdf's (or pmf's). We
assume that the set fx : f(x; �) > 0g is independent of �, where x = (x1; : : : ; xn). We say that
the family ff(�; �) : � 2 #g is a one{parameter exponential family if there exist real{valued
functions Q(�) and D(�) on # and Borel{measurable functions T (X) and S(X) on IRn such that
f(x; �) = exp(Q(�)T (x) +D(�) + S(x)):
Note:
We can also write f(x; �) as
f(x; �) = h(x)c(�) exp(�T (x))
where h(x) = exp(S(x)), � = Q(�), and c(�) = exp(D(Q�1(�))), and call this the exponential
family in canonical form for a natural parameter �.
De�nition 5.2.2:
Let # � IRk be a k{dimensional interval. Let ff(�; �) : � 2 #g be a family of pdf's (or pmf's). We
assume that the set fx : f(x; �) > 0g is independent of �, where x = (x1; : : : ; xn). We say that the
family ff(�; �) : � 2 #g is a k{parameter exponential family if there exist real{valued functions
Q1(�); : : : Qk(�) and D(�) on # and Borel{measurable functions T1(X); : : : ; Tk(X) and S(X) on
IRn such that
f(x; �) = exp
kXi=1
Qi(�)Ti(x) +D(�) + S(x)
!:
Note:
Similar to the Note following De�nition 5.2.1, we can express the k{parameter exponential family
in canonical form for a natural k � 1 parameter vector � = (�1; : : : ; �k)0.
Example 5.2.3:
Let X � N(�; �2) with both parameters � and �2 unknown. We have:
f(x; �) =1p2��2
exp
�� 1
2�2(x� �)2
�= exp
� 1
2�2x2 +
�
�2x� �
2
2�2� 1
2ln(2��2)
!
� = (�; �2)
# = f(�; �2) : � 2 IR; �2> 0g
120
Therefore,
Q1(�) = � 1
2�2
T1(x) = x2
Q2(�) =�
�2
T2(x) = x
D(�) = � �2
2�2� 1
2ln(2��2)
S(x) = 0
Thus, this is a 2{parameter exponential family.
121
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 36: Monday 11/22/1999
Scribe: J�urgen Symanzik, Hanadi B. Eltahir
6 Limit Theorems
Motivation:
I found this slide from my Stat 250, Section 003, \Introductory Statistics" class (an undergraduate
class I taught at George Mason University in Spring 1999):
What does this mean at a more theoretical level???
122
6.1 Modes of Convergence
De�nition 6.1.1:
Let X1; : : : ;Xn be iid rv's with common cdf FX(x). Let T = T (X) be any statistic, i.e., a Borel{
measurable function of X that does not involve the population parameter(s) #, de�ned on the
support X of X . The induced probability distribution of T (X) is called the sampling distribu-
tion of T (X).
Note:
(i) Commonly used statistics are:
Sample Mean: Xn =1n
nXi=1
Xi
Sample Variance: S2n =
1n�1
nXi=1
(Xi �Xn)2
Sample Median, Order Statistics, Min, Max, etc.
(ii) Recall that if X1; : : : ;Xn are iid and if E(X) and V ar(X) exist, then E(Xn) = � = E(X),
E(S2n) = �
2 = V ar(X), and V ar(Xn) =�2
n .
(iii) Recall that if X1; : : : ; Xn are iid and if X has mgf MX(t) or characteristic function �X(t)
then MXn(t) = (MX(
tn))
n or �Xn(t) = (�X(
tn))
n.
Note: Let fXng1n=1 be a sequence of rv's on some probability space (; L; P ). Is there any meaning
behind the expression limn!1Xn = X? Not immediately under the usual de�nitions of limits. We
�rst need to de�ne modes of convergence for rv's and probabilities.
De�nition 6.1.2:
Let fXng1n=1 be a sequence of rv's with cdf's fFng1n=1 and letX be a rv with cdf F . If Fn(x)! F (x)
at all continuity points of F , we say that Xn converges in distribution to X (Xnd�! X) or
Xn converges in law to X (XnL�! X), or Fn converges weakly to F (Fn
w�! F ).
Example 6.1.3:
Let Xn � N(0; 1n). Then
Fn(x) =
Z x
�1
exp��1
2nt2�
q2�n
dt
123
=
Z pnx
�1
exp(�12s
2)p2�
ds
= �(pnx)
=) Fn(x) !
8>><>>:
�(1) = 1; if x > 0
�(0) = 12 ; if x = 0
�(�1) = 0; if x < 0
If FX(x) =
(1; x � 0
0; x < 0the only point of discontinuity is at x = 0. Everywhere else,
�(pnx) = Fn(x)! FX(x).
So, Xnd�! X, where P (X = 0) = 1, or Xn
d�! 0 since the limiting rv here is degenerate, i.e., it
has a Dirac(0) distribution.
Example 6.1.4:
In this example, the sequence fFng1n=1 converges pointwise to something that is not a cdf:
Let Xn � Dirac(n), i.e., P (Xn = n) = 1. Then,
Fn(x) =
(0; x < n
1; x � n
It is Fn(x)! 0 8x which is not a cdf. Thus, there is no rv X such that Xnd�! X.
Example 6.1.5:
Let fXng1n=1 be a sequence of rv's such that P (Xn = 0) = 1 � 1nand P (Xn = n) = 1
nand let
X � Dirac(0), i.e., P (X = 0) = 1.
It is
Fn(x) =
8>><>>:
0; x < 0
1� 1n ; 0 � x < n
1; x � n
FX(x) =
(0; x < 0
1; x � 0
It holds that Fnw�! FX but
E(Xkn) = n
k�1 6! E(Xk) = 0:
Thus, convergence in distribution does not imply convergence of moments/means.
124
Note:
Convergence in distribution does not say that the Xi's are close to each other or to X. It only
means that their cdf's are (eventually) close to some cdf F . The Xi's do not even have to be de�ned
on the same probability space.
Example 6.1.6:
Let X and fXng1n=1 be iid N(0; 1). Obviously, Xnd�! X but lim
n!1Xn 6= X.
Theorem 6.1.7:
Let X and fXng1n=1 be discrete rv's with support X and fXng1n=1, respectively. De�ne the count-
able set A = X[1[n=1
Xn = fak : k = 1; 2; 3; : : :g. Let pk = P (X = ak) and pnk = P (Xn = ak). Then
it holds that pnk ! pk 8k i� Xnd�! X.
Theorem 6.1.8:
Let X and fXng1n=1 be continuous rv's with pdf's f and ffng1n=1, respectively. If fn(x)! f(x) for
almost all x as n!1 then Xnd�! X.
Theorem 6.1.9:
Let X and fXng1n=1 be rv's such that Xnd�! X. Let c 2 IR be a constant. Then it holds:
(i) Xn + cd�! X + c.
(ii) cXnd�! cX.
(iii) If an ! a and bn ! b, then anXn + bnd�! aX + b.
Proof:
Part (iii):
Suppose that a > 0; an > 0. Let Yn = anXn + bn and Y = aX + b. It is
FY (y) = P (Y < y) = P (aX + b < y) = P (X <y � b
a) = FX(
y � b
a):
Likewise,
FYn(y) = FXn(y � bn
an):
If y is a continuity point of FY ,y�ba
is a continuity point of FX . Since an ! a; bn ! b and
FXn(x) ! FX(x), it follows that FYn(y) ! FY (y) for every continuity point y of FY . Thus,
anXn + bnd�! aX + b.
125
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 37: Wednesday 11/24/1999
Scribe: J�urgen Symanzik, Rich Madsen
De�nition 6.1.10:
Let fXng1n=1 be a sequence of rv's de�ned on a probability space (; L; P ). We say that Xn
converges in probability to a rv X (Xnp�! X, P- lim
n!1Xn = X) if
limn!1P (j Xn �X j> �) = 0 8� > 0:
Note:
The following are equivalent:
limn!1P (j Xn�X j> �) = 0, lim
n!1P (j Xn�X j� �) = 1, limn!1P (f! :j Xn(!)�X(!) j> �)) = 0:
If X is degenerate, i.e., P (X = c) = 1, we say that Xn is consistent for c. For example, let Xn
such that P (Xn = 0) = 1� 1n and P (Xn = 1) = 1
n . Then
P (j Xn j> �) =
(1n; 0 < � < 1
0; � � 1
Therefore, limn!1P (j Xn j> �) = 0 8� > 0. So Xn
p�! 0, i.e., Xn is consistent for 0.
Theorem 6.1.11:
(i) Xnp�! X () Xn �X
p�! 0.
(ii) Xnp�! X;Xn
p�! Y =) P (X = Y ) = 1.
(iii) Xnp�! X;Xm
p�! X =) Xn �Xmp�! 0 as n;m!1.
(iv) Xnp�! X;Yn
p�! Y =) Xn � Ynp�! X � Y .
(v) Xnp�! X; k 2 IR a constant =) kXn
p�! kX.
(vi) Xnp�! k; k 2 IR a constant =) X
rn
p�! kr 8r 2 IN .
(vii) Xnp�! a; Yn
p�! b; a; b 2 IR =) XnYnp�! ab.
(viii) Xnp�! 1 =) X
�1n
p�! 1.
126
(ix) Xnp�! a; Yn
p�! b; a 2 IR; b 2 IR� f0g =) Xn
Yn
p�! ab.
(x) Xnp�! X;Y an arbitrary rv =) XnY
p�! XY .
(xi) Xnp�! X;Yn
p�! Y =) XnYnp�! XY .
Proof:
See Rohatgi, page 244{245 for partial proofs.
Theorem 6.1.12:
Let Xnp�! X and let g be a continuous function on IR. Then g(Xn)
p�! g(X).
Proof:
Preconditions:
1.) X rv =) 8� > 0 9k = k(�) : P (jXj > k) < �2
2.) g is continuous on IR
=) g is also uniformly continuous (see De�nition of u.c. in Theorem 3.3.3 (iii)) on [�k; k]
=) 9� = �(�; k) : jXj � k; jXn �Xj < � ) jg(Xn)� g(X)j < �
Let
A = fjXj � kg = f! : jX(!)j � kg
B = fjXn �Xj < �g = f! : jXn(!)�X(!)j < �g
C = fjg(Xn)� g(X)j < �g = f! : jg(Xn(!))� g(X(!))j < �g
If ! 2 A \B2:)=) ! 2 C
=) A \B � C
=) CC � (A \B)C = A
C [BC
=) P (CC) � P (AC [BC) � P (AC) + P (BC)
Now:
P (jg(Xn)� g(X)j � �) � P (jXj > k)| {z }� �2by 1.)
+ P (jXn �Xj � �)| {z }� �2for n�n0(�;�;k) since Xn
p�!X
� � for n � n0(�; �; k)
127
Corollary 6.1.13:
Let Xnp�! c; c 2 IR and let g be a continuous function on IR. Then g(Xn)
p�! g(c).
Theorem 6.1.14:
Xnp�! X =) Xn
d�! X.
Proof:
Xnp�! X , P (jXn �Xj > �)! 0 as n!1 8� > 0
It holds:
P (X � x� �) = P (X � x� �; jXn �Xj � �) + P (X � x� �; jXn �Xj > �)
(A)
� P (Xn � x) + P (jXn �Xj > �)
(A) holds since X � x� � and Xn within � of X, thus Xn � x.
Similarly, it holds:
P (Xn � x) � P (X � x+ �) + P (jXn �Xj > �)
= P (Xn � x; j Xn �X j� �) + P (Xn � x; j Xn �X j> �)
Combining the 2 inequalities from above gives:
P (X � x� �)� P (jXn �Xj > �)| {z }!0 as n!1
� P (Xn � x)| {z }=Fn(x)
� P (X � x+ �) + P (jXn �Xj > �)| {z }!0 as n!1
Therefore,
P (X � x� �) � Fn(x) � P (X � x+ �) as n!1:
Since the cdf's Fn(�) are not necessarily left continuous, we get the following result for � # 0:
P (X < x) � Fn(x) � P (X � x) = FX(x)
Let x be a continuity point of F . Then it holds:
F (x) = P (X < x) � Fn(x) � F (x)
=) Fn(x)! F (x)
=) Xnd�! X
128
Theorem 6.1.15:
Let c 2 IR be a constant. Then it holds:
Xnd�! c() Xn
p�! c:
Example 6.1.16:
In this example, we will see that
Xnd�! X 6=) Xn
p�! X
for some rv X. Let Xn be identically distributed rv's and let (Xn;X) have the following joint
distribution:Xn
X0 1
0 0 12
12
1 12 0 1
212
12 1
Obviously, Xnd�! X since all have exactly the same cdf, but for any � 2 (0; 1), it is
P (j Xn �X j> �) = P (j Xn �X j= 1) = 1 8n;
so limn!1P (j Xn �X j> �) 6= 0. Therefore, Xn 6
p�! X.
Theorem 6.1.17:
Let fXng1n=1 and fYng1n=1 be sequences of rv's and X be a rv de�ned on a probability space
(; L; P ). Then it holds:
Ynd�! X; j Xn � Yn j
p�! 0 =) Xnd�! X:
Proof:
Similar to the proof of Theorem 6.1.14. See also Rohatgi, page 253, Theorem 14.
129
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 38: Monday 11/29/1999
Scribe: J�urgen Symanzik, Bill Morphet
Theorem 6.1.18: Slutsky's Theorem
Let (Xn)1n=1 and (Yn)
1n=1 be sequences of rv's andX be a rv de�ned on a probability space (; L; P ).
Let c 2 IR be a constant. Then it holds:
(i) Xnd�! X;Yn
p�! c =) Xn + Ynd�! X + c.
(ii) Xnd�! X;Yn
p�! c =) XnYnd�! cX.
If c = 0, then also XnYnp�! 0.
(iii) Xnd�! X;Yn
p�! c =) Xn
Yn
d�! Xc if c 6= 0.
Proof:
(i) Ynp�! c
Th:6:1:11(i)() Yn � cp�! 0
=) Yn � c = Yn + (Xn �Xn)� c = (Xn + Yn)� (Xn + c)p�! 0 (A)
Xnd�! X
Th:6:1:9(i)=) Xn + c
d�! X + c (B)
Combining (A) and (B), it follows from Theorem 6.1.17:
Xn + Ynd�! X + c
(ii) Case c = 0:
8� > 0 8k > 0, it is
P (j XnYn j> �) = P (j XnYn j> �; Yn ��
k) + P (j XnYn j> �; Yn >
�
k)
� P (j Xn�
kj> �) + P (Yn >
�
k)
� P (j Xn j> k) + P (j Yn j>�
k)
Since Xnd�! X and Yn
p�! 0, it follows
limn!1P (j XnYn j> �) � P (j Xn j> k)! 0 as k !1:
Therefore,
XnYnp�! 0:
130
Case c 6= 0:
Since Xnd�! X and Yn
p�! c, it follows XnYn � cXn = Xn(Yn � c)p�! 0.
=) XnYnp�! cXn
Th:6:1:14=) XnYn
d�! cXn
Since cXnd�! cX by Theorem 6.1.9 (ii), it follows from Theorem 6.1.17:
XnYnd�! cX
(iii) Let Znp�! 1 and let Yn = cZn.
c6=0=) 1
Yn= 1
Zn� 1c
Th:6:1:11(v;viii)=) 1
Yn
p�! 1c
With part (ii) above, it follows:
Xnd�! X and 1
Yn
p�! 1c
=) Xn
Yn
d�! Xc
De�nition 6.1.19:
Let (Xn)1n=1 be a sequence of rv's such that E(j Xn jr) < 1 for some r > 0. We say that Xn
converges in the rth mean to a rv X (Xn
r�! X) if E(j X jr) <1 and
limn!1E(j Xn �X jr) = 0:
Example 6.1.20:
Let (Xn)1n=1 be a sequence of rv's de�ned by P (Xn = 0) = 1� 1
nand P (Xn = 1) = 1
n.
It is E(j Xn jr) = 1n 8r > 0. Therefore, Xn
r�! 0 8r > 0.
Note:
The special cases r = 1 and r = 2 are called convergence in absolute mean for r = 1 (Xn1�! X)
and convergence in mean square for r = 2 (Xnms�! X or Xn
2�! X).
Theorem 6.1.21:
Assume that Xnr�! X for some r > 0. Then Xn
p�! X.
Proof:
Using Markov's Inequality (Corollary 3.5.2), it holds for any � > 0:
E(j Xn �X jr)�r
� P (j Xn �X j� �)
131
Xnr�! X =) lim
n!1E(j Xn �X jr) = 0
=) limn!1P (j Xn �X j� �) � lim
n!1E(j Xn �X jr)
�r= 0
=) Xnp�! X
Example 6.1.22:
Let (Xn)1n=1 be a sequence of rv's de�ned by P (Xn = 0) = 1 � 1
nrand P (Xn = n) = 1
nrfor some
r > 0.
For any � > 0, P (j Xn j> �)! 0 as n!1; so Xnp�! 0.
For 0 < s < r, E(j Xn js) = 1nr�s ! 0 as n!1; so Xn
s�! 0. But E(j Xn jr) = 1 6! 0 as n!1;
so Xn 6r�! 0.
Theorem 6.1.23:
If Xnr�! X, then it holds:
(i) limn!1E(j Xn jr) = E(j X jr); and
(ii) Xns�! X for 0 < s < r.
Proof:
(i) For 0 < r � 1, it holds:
E(j Xn jr) = E(j Xn �X +X jr) � E(j Xn �X jr + j X jr)
=) E(j Xn jr)�E(j X jr) � E(j Xn �X jr)
=) limn!1E(j Xn jr)� lim
n!1E(j X jr) � limn!1E(j Xn �X jr) = 0
=) limn!1E(j Xn jr) � E(j X jr) (A)
Similarly,
E(j X jr) = E(j X �Xn +Xn jr) � E(j Xn �X jr + j Xn jr)
=) E(j X jr)�E(j Xn jr) � E(j Xn �X jr)
=) limn!1E(j X jr)� lim
n!1E(j Xn jr) � limn!1E(j Xn �X jr) = 0
=) E(j X jr) � limn!1E(j Xn jr) (B)
Combining (A) and (B) gives
limn!1E(j Xn jr) = E(j X jr)
132
For r > 1, it follows from Minkowski's Inequality (Theorem 4.8.3):
[E(j X �Xn +Xn jr)]1r � [E(j X �Xn jr)]
1r + [E(j Xn jr)]
1r
=) [E(j X jr)] 1r � [E(j Xn jr)]1r � [E(j X �Xn jr)]
1r
=) [E(j X jr)] 1r � limn!1[E(j Xn jr)]
1r � lim
n!1[E(j Xn �X jr)]1r = 0 since Xn
r�! X
=) [E(j X jr)] 1r � limn!1[E(j Xn jr)]
1r (C)
Similarly,
[E(j Xn �X +X jr)] 1r � [E(j Xn �X jr)] 1r + [E(j X jr)] 1r
=) limn!1[E(j Xn jr)]
1r� lim
n!1[E(j X jr)]1r � lim
n!1[E(j Xn�X jr)]1r = 0 since Xn
r�! X
=) limn!1[E(j Xn jr)]
1r � [E(j X jr)]
1r (D)
Combining (C) and (D) gives
limn!1[E(j Xn jr)]
1r = [E(j X jr)]
1r
=) limn!1E(j Xn jr) = E(j X jr)
(ii) For 1 � s < r, it follows from Lyapunov's Inequality (Theorem 3.5.4):
[E(j Xn �X js)] 1s � [E(j Xn �X jr)] 1r
=) E(j Xn �X js) � [E(j Xn �X jr)] sr
=) limn!1E(j Xn �X js) � lim
n!1[E(j Xn �X jr)]sr = 0 since Xn
r�! X
=) Xns�! X
An additional proof is required for 0 < s < r � 1.
133
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lectures 39 & 40: Wednesday 12/1/1999 & Friday 12/3/1999
Scribe: J�urgen Symanzik, Hanadi B. Eltahir
De�nition 6.1.24:
Let fXng1n=1 be a sequence of rv's on (; L; P ). We say that Xn converges almost surely to
a rv X (Xna:s:�! X) or Xn converges with probability 1 to X (Xn
w:p:1�! X) or Xn converges
strongly to X i�
P (f! : Xn(!)! X(!) as n!1g) = 1:
Note:
An interesting characterization of convergence with probability 1 and convergence in probability
can be found in Parzen (1960) \Modern Probability Theory and Its Applications" on page 416 (see
Handout).
Example 6.1.25:
Let = [0; 1] and P a uniform distribution on . Let Xn(!) = ! + !n and X(!) = !.
For ! 2 [0; 1), !n ! 0 as n!1. So Xn(!)! X(!) 8! 2 [0; 1).
However, for ! = 1, Xn(1) = 2 6= 1 = X(1) 8n, i.e., convergence fails at ! = 1.
Anyway, since P (f! : Xn(!)! X(!) as n!1g) = P (f! 2 [0; 1)g) = 1, it is Xna:s:�! X.
Theorem 6.1.26:
Xna:s:�! X =) Xn
p�! X.
Proof:
Choose � > 0 and � > 0. Find n0 = n0(�; �) such that
P
1\n=n0
fj Xn �X j� �g!� 1� �:
Since1\
n=n0
fj Xn �X j� �g � fj Xn �X j� �g 8n � n0, it is
P (fj Xn �X j� �g) � P
1\n=n0
fj Xn �X j� �g!� 1� � 8n � n0:
Therefore, P (fj Xn �X j� �g)! 1 as n!1. Thus, Xnp�! X.
134
Example 6.1.27:
Xnp�! X 6=) Xn
a:s:�! X:
Let = (0; 1] and P a uniform distribution on .
De�ne An by
A1 = (0; 12 ]; A2 = (12 ; 1]
A3 = (0; 14 ]; A4 = (14 ;12 ]; A5 = (12 ;
34 ]; A6 = (34 ; 1]
A7 = (0; 18 ]; A8 = (18 ;14 ]; : : :
Let Xn(!) = IAn(!).
It is P (j Xn � 0 j� �)! 0 8� > 0 since Xn is 0 except on An and P (An) # 0. Thus Xnp�! 0.
But P (f! : Xn(!) ! 0g) = 0 (and not 1) because any ! keeps being in some An beyond any n0,
i.e., Xn(!) looks like 0 : : : 010 : : : 010 : : : 010 : : :, so Xn 6a:s:�! 0.
Example 6.1.28:
Xnr�! X 6=) Xn
a:s:�! X:
Let Xn be independent rv's such that P (Xn = 0) = 1� 1n and P (Xn = 1) = 1
n .
It is E(j Xn � 0 jr) = E(j Xn jr) = E(j Xn j) = 1n ! 0 as n!1, so Xn
r�! 0 8r > 0.
But
P (Xn = 0 8m � n � n0) =
n0Yn=m
(1� 1
n) = (
m� 1
m)(
m
m+ 1)(m+ 1
m+ 2) : : : (
n0 � 2
n0 � 1)(n0 � 1
n0) =
m� 1
n0
As n0 !1, it is P (Xn = 0 8m � n � n0)! 0 8m, so Xn 6a:s:�! 0.
Example 6.1.29:
Xna:s:�! X 6=) Xn
r�! X:
Let = [0; 1] and P a uniform distribution on .
Let An = [0; 1lnn ].
Let Xn(!) = nIAn(!) and X(!) = 0.
It holds that 8! > 0 9n0 : 1lnn0
< ! =) Xn(!) = 0 8n > n0 and P (! = 0) = 0. Thus, Xna:s:�! 0.
But E(j Xn � 0 jr) = nr
lnn !1 8r > 0, so Xn 6r�! X.
135
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 41: Monday 12/6/1999
Scribe: J�urgen Symanzik
6.2 Weak Laws of Large Numbers
Theorem 6.2.1: WLLN: Version I
Let fXig1i=1 be a sequence of iid rv's with mean E(Xi) = � and variance V ar(Xi) = �2<1. Let
Xn =1n
nXi=1
Xi. Then it holds
limn!1P (j Xn � � j� �) = 0;
i.e., Xnp�! �.
Proof:
By Markov's Inequality, it holds for all � > 0:
P (j Xn � � j� �) � E((Xn��)2)�2
=V ar(Xn)
�2= �2
n�2�! 0 as n!1
Note:
For iid rv's with �nite variance, Xn is consistent for �.
A more general way to derive a \WLLN" follows in the next De�nition.
De�nition 6.2.2:
Let fXig1i=1 be a sequence of rv's. Let Tn =nXi=1
Xi. We say that fXig obeys the WLLN with
respect to a sequence of norming constants fBig1i=1, Bi > 0; Bi " 1, if there exists a sequence
of centering constants fAig1i=1 such that
B�1n (Tn �An)
p�! 0:
Theorem 6.2.3:
Let fXig1i=1 be a sequence of pairwise uncorrelated rv's with E(Xi) = �i and V ar(Xi) = �2i , i 2 IN .
IfnXi=1
�2i !1 as n!1, we can choose An =
nXi=1
�i and Bn =nXi=1
�2i and get
nXi=1
(Xi � �i)
nXi=1
�2i
p�! 0:
136
Proof:
By Markov's Inequality, it holds for all � > 0:
P (jnXi=1
Xi �nXi=1
�i j> �
nXi=1
�2i ) �
E((nXi=1
(Xi � �))2)
�2(nXi=1
�2i )
2
=1
�2nXi=1
�2i
�! 0 as n!1
Note:
To obtain Theorem 6.2.1, we choose An = n� and Bn = n�2.
Theorem 6.2.4:
Let fXig1i=1 be a sequence of rv's. Let Xn = 1n
nXi=1
Xi. A necessary and su�cient condition for
fXig to obey the WLLN with respect to Bn = n is that
E
X
2n
1 +X2n
!! 0
as n!1.
Proof:
Rohatgi, page 258, Theorem 2.
Example 6.2.5:
Let (X1; : : : ;Xn) be jointly Normal with E(Xi) = 0, E(X2i ) = 1 for all i, and Cov(Xi; Xj) = � if
j i� j j= 1 and Cov(Xi;Xj) = 0 if j i� j j> 1. Then, Tn � N(0; n+ 2(n� 1)�) = N(0; �2). It is
E
X
2n
1 +X2n
!= E
T2n
n2 + T 2n
!
=2p2��
Z 1
0
x2
n2 + x2e� x2
2�2 dx j y =x
�; dy =
dx
�
=2p2�
Z 1
0
�2y2
n2 + �2y2e� y2
2 dy
=2p2�
Z 1
0
(n+ 2(n� 1)�)y2
n2 + (n+ 2(n� 1)�)y2e� y2
2 dy
� n+ 2(n� 1)�
n2
Z 1
0
2p2�
y2e� y2
2 dy| {z }=1; since Var of N(0;1) distribution
! 0 as n!1
=) Xnp�! 0
137
Note:
We would like to have a WLLN that just depends on means but does not depend on the existence
of �nite variances. To approach this, we consider the following:
Let fXig1i=1 be a sequence of rv's. Let Tn =nXi=1
Xi. We truncate each Xi at c > 0 and get
Xci =
(Xi; j Xi j� c
0; otherwise
Let T cn =
nXi=1
Xci and mn =
nXi=1
E(Xci ).
Lemma 6.2.6:
For Tn, Tcn and mn as de�ned in the Note above, it holds:
P (j Tn �mn j> �) � P (j T cn �mn j> �) +
nXi=1
P (j Xi j> c) 8� > 0
Proof:
It holds for all � > 0:
P (j Tn �mn j> �) = P (j Tn �mn j> � and j Xi j� c 8i 2 f1; : : : ; ng) +
P (j Tn �mn j> � and j Xi j> c for at least one i 2 f1; : : : ; ng(�)� P (j T c
n �mn j> �) + P (j Xi j> c for at least one i 2 f1; : : : ; ng)
� P (j T cn �mn j> �) +
nXi=1
P (j Xi j> c)
(�) holds since T cn = Tn when j Xi j� c 8i 2 f1; : : : ; ng.
Note:
If the Xi's are identically distributed, then
P (j Tn �mn j> �) � P (j T cn �mn j> �) + nP (j X1 j> c) 8� > 0:
If the Xi's are iid, then
P (j Tn �mn j> �) � nE((Xc1)
2)
�2+ nP (j X1 j> c) 8� > 0 (�):
Note that P (j Xi j> c) = P (j X1 j> c) 8i 2 IN if the Xi's are identically distributed and that
E((Xci )
2) = E((Xc1)
2) 8i 2 IN if the Xi's are iid.
138
Theorem 6.2.7: Khintchine's WLLN
Let fXig1i=1 be a sequence of iid rv's with �nite mean E(Xi) = �. Then it holds:
Xn =1
nTn
p�! �
Proof:
If we take c = n and replace � by n� in (�) in the Note above, we get
P (j Tn �mn j> n�) � E((Xn1 )
2)
n�2+ nP (j X1 j> n):
Since E(j X1 j) <1, it is nP (j X1 j> n)! 0 as n!1 by Theorem 3.1.9. From Corollary 3.1.12
we know that E(j X j�) = �
Z 1
0x��1
P (j X j> x)dx. Therefore,
E((Xn1 )
2) = 2
Z n
0xP (j Xn
1 j> x)dx
= 2
Z A
0xP (j Xn
1 j> x)dx+ 2
Z n
AxP (j Xn
1 j> x)dx
(+)
� K + �
Z n
Adx
� K + n�
In (+), A is chosen su�ciently large such that xP (j Xn1 j> x) < �
2 8x � A for an arbitrary constant
� > 0 and K > 0 a constant.
Therefore,E((Xn
1 )2)
n�2� k
n�2+
�
�2
Since � is arbitrary, we can make the right hand side of this last inequality arbitrarily small for
su�ciently large n.
Since E(Xi) = � 8i, it is mn
n! � as n!1.
Note:
Theorem 6.2.7 meets the previously stated goal of not having a �nite variance requirement.
139
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 42: Wednesday 12/8/1999
Scribe: J�urgen Symanzik
6.3 Strong Laws of Large Numbers
De�nition 6.3.1:
Let fXig1i=1 be a sequence of rv's. Let Tn =nXi=1
Xi. We say that fXig obeys the SLLN with
respect to a sequence of norming constants fBig1i=1, Bi > 0; Bi " 1, if there exists a sequence
of centering constants fAig1i=1 such that
B�1n (Tn �An)
a:s:�! 0:
Note:
Unless otherwise speci�ed, we will only use the case that Bn = n in this section.
Theorem 6.3.2:
Xna:s:�! X () lim
n!1P ( supm�n
j Xm �X j> �) = 0 8� > 0.
Proof: (see also Rohatgi, page 249, Theorem 11)
WLOG, we can assume that X = 0 since Xna:s:�! X implies Xn�X
a:s:�! 0. Thus, we have to prove:
Xna:s:�! 0 () lim
n!1P ( supm�n
j Xm j> �) = 0 8� > 0
\=)":
Choose � > 0 and de�ne
An(�) = f supm�n
j Xm j> �g
C = f limn!1Xn = 0g
We know that P (C) = 1 and therefore P (Cc) = 0.
Let Bn(�) = C \An(�). Note that Bn+1(�) � Bn(�) and for the limit set1\n=1
Bn(�) = �. It follows
that
limn!1P (Bn(�)) = P (
1\n=1
Bn(�)) = 0:
140
We also have
P (Bn(�)) = P (An \ C)
= 1� P (Cc [Acn)
= 1� P (Cc)| {z }=0
�P (Acn) + P (Cc \AC
n )| {z }=0
= P (An)
=) limn!1P (An(�)) = 0
\(=":Assume that lim
n!1P (An(�)) = 0 8� > 0 and de�ne D(�) = f limn!1 j Xn j> �g. Since D(�) �
An(�) 8n 2 IN , it follows that P (D(�)) = 0 8� > 0. Also,
Cc = f lim
n!1Xn 6= 0g �1[k=1
f limn!1 j Xn j>
1
kg:
=) 1� P (C) �1Xk=1
P (D(1
k)) = 0
=) Xna:s:�! 0
Note:
(i) Xna:s:�! 0 implies that 8� > 0 8� > 0 9n0 2 IN : P ( sup
n�n0j Xn j> �) < �.
(ii) Recall that for a given sequence of events fAng1n=1,
A = limn!1An = lim
n!1
1[k=n
Ak =1\n=1
1[k=n
Ak
is the event that in�nitely many of the An occur. We write P (A) = P (An i:o:) where i:o:
stands for \in�nitely often".
(iii) Using the terminology de�ned in (ii) above, we can rewrite Theorem 6.3.2 as
Xna:s:�! 0 () P (j Xn j> � i:o:) = 0 8� > 0:
141
Theorem 6.3.3: Borel{Cantelli Lemma
(i) 1st BC{Lemma:
Let fAng1n=1 be a sequence of events such that1Xn=1
P (An) <1. Then P (A) = 0.
(ii) 2nd BC{Lemma:
Let fAng1n=1 be a sequence of independent events such that1Xn=1
P (An) =1. Then P (A) = 1.
Proof:
(i):
P (A) = P ( limn!1
1[k=n
Ak)
= limn!1P (
1[k=n
Ak)
� limn!1
1Xk=n
P (Ak)
= limn!1
1Xk=1
P (Ak)�n�1Xk=1
P (Ak)
!
= 0
(ii): We have Ac =1[n=1
1\k=n
Ack. Therefore,
P (Ac) = P ( limn!1
1\k=n
Ack) = lim
n!1P (1\k=n
Ack):
If we choose n0 > n, it holds that1\k=n
Ack �
n0\k=n
Ack:
Therefore,
P (1\k=n
Ack) � lim
n0!1P (
n0\k=n
Ack)
= limn0!1
n0Yk=n
(1� P (Ak))
indep:
� limn0!1 exp
�
n0Xk=n
P (Ak)
!
= 0
=) P (A) = 1
142
Example 6.3.4:
Independence is necessary for 2nd BC{Lemma:
Let = (0; 1) and P a uniform distribution on .
Let An = I(0; 1n)(!). Therefore,
1Xn=1
P (An) =1Xn=1
1
n=1:
But for any ! 2 , An occurs only for 1; 2; : : : ; b 1! c, where b1! c denotes the largest integer (\ oor")
that is � 1!. Therefore, P (A) = P (An i:o:) = 0.
Lemma 6.3.5: Kolmogorov's Inequality
Let fXig1i=1 be a sequence of independent rv's with common mean 0 and variances �2i . Let Tn =nXi=1
Xi. Then it holds:
P ( max1�k�n
j Tk j� �) �
nXi=1
�2i
�28� > 0
Proof:
See Rohatgi, page 268, Lemma 2.
Lemma 6.3.6: Kronecker's Lemma
If1Xi=1
Xi converges to s <1 and Bn " 1, then it holds:
1
Bn
nXi=1
BkXk ! 0
Proof:
See Rohatgi, page 269, Lemma 3.
Theorem 6.3.7: Cauchy Criterion
Xna:s:�! X () lim
n!1P (supm
j Xn+m �Xn j� �) = 1 8� > 0.
Proof:
See Rohatgi, page 270, Theorem 5.
143
Theorem 6.3.8:
If1Xn=1
V ar(Xn) <1, then1Xn=1
(Xn �E(Xn)) converges almost surely.
Proof:
See Rohatgi, page 272, Theorem 6.
144
Stat 6710 Mathematical Statistics I Fall Semester 1999
Lecture 43: Friday 12/10/1999
Scribe: J�urgen Symanzik
Corollary 6.3.9:
Let fXig1i=1 be a sequence of independent rv's. Let fBig1i=1, Bi > 0; Bi " 1, a sequence of norming
constants. Let Tn =nXi=1
Xi. If1Xi=1
V ar(Xi)
B2i
<1 then it holds:
Tn �E(Tn)
Bn
a:s:�! 0
Proof:
This Corollary follows directly from Theorem 6.3.8 and Lemma 6.3.6.
Lemma 6.3.10: Equivalence Lemma
Let fXig1i=1 and fX 0ig1i=1 be sequences of rv's. Let Tn =
nXi=1
Xi and T0n =
nXi=1
X0i.
If the series1Xi=1
P (Xi 6= X0i) <1, then the series fXig and fX 0
ig are tail{equivalent and Tn and
T0n are convergence{equivalent, i.e., for Bn " 1 the sequences 1
BnTn and 1
BnT0n converge on the
same event and to the same limit, except for a null set.
Proof:
See Rohatgi, page 266, Lemma 1.
Lemma 6.3.11:
Let X be a rv with E(j X j) <1. Then it holds:
1Xn=1
P (j X j� n) � E(j X j) � 1 +1Xn=1
P (j X j� n)
Proof:
Continuous case only:
Let X have a pdf f . Then it holds:
E(j X j) =Z 1
�1j x j f(x)dx =
1Xk=0
Zk�jxj�k+1
j x j f(x)dx
=)1Xk=0
kP (k �j X j� k + 1) � E(j X j) �1Xk=0
(k + 1)P (k �j X j� k + 1)
145
It is
1Xk=0
kP (k �j X j� k + 1) =1Xk=0
kXn=1
P (k �j X j� k + 1)
=1Xn=1
1Xk=n
P (k �j X j� k + 1)
=1Xn=1
P (j X j� n)
Similarly,
1Xk=0
(k + 1)P (k �j X j� k + 1) =1Xn=1
P (j X j� n) +1Xk=0
P (k �j X j� k + 1)
=1Xn=1
P (j X j� n) + 1
Theorem 6.3.12: Kolmogorov's SLLN
Let fXig1i=1 be a sequence of iid rv's. Let Tn =nXi=1
Xi. Then it holds:
Tn
n= Xn
a:s:�! � <1 () E(j X j) <1 (and then � = E(X))
Proof:
\=)":
Suppose that Xna:s:�! � <1. It is
Tn =nXi=1
Xi =n�1Xi=1
Xi +Xn = Tn�1 +Xn.
=) Xn
n =Tn
n|{z}a:s:�!�
� n� 1
n| {z }!1
Tn�1n� 1| {z }a:s:�!�
a:s:�! 0
By 1st Borel{Cantelli Lemma, we must have1Xn=1
P (j Xn j� n) <1.
Lemma 6:3:11=) E(j X j) <1
Th: 6:2:7 (WLLN)=) Xn
p�! E(X)
Since Xna:s:�! �, it holds that Xn
p�! �. Therefore, it must hold that � = E(X).
146
\(=":Let E(j X j) <1. De�ne truncated rv's:
X0k =
(Xk; if j Xk j� k
0; otherwise
T0n =
nXk=1
X0k
X0n =
T0n
n
Then it holds:
1Xk=1
P (Xk 6= X0k) =
1Xk=1
P (j Xk j� k)
iid=
1Xk=1
P (j X j� k)
Lemma 6:3:11< E(j X j)
< 1
By Lemma 6.3.10, it follows that Tn and T0n are convergence{equivalent. Thus, it is su�cient to
prove that X0n
a:s:�! E(X).
We now establish the conditions needed in Corollary 6.3.9. It is
V ar(X 0n) � E((X 0
n)2)
=
Z n
�nx2fX(x)dx
=n�1Xk=0
Zk�jxj<k+1
x2fX(x)dx
�n�1Xk=0
(k + 1)2P (k �j X j< k + 1)
=)1Xn=1
1
n2V ar(X 0
n) �1Xn=1
nXk=0
(k + 1)2
n2P (k �j X j< k + 1)
=1Xn=1
nXk=1
(k + 1)2
n2P (k �j X j< k + 1) +
1Xn=1
1
n2P (0 �j X j< 1)
(�)=
1Xk=1
(k + 1)2P (k �j X j< k + 1)
1Xn=k
1
n2
!+ 2P (0 �j X j< 1) (A)
147
(�) holds since1Xn=1
1
n2=
�2
6� 1:65 < 2 and the �rst two sums can be rearranged as follows:
n k
1 1
2 1; 2
3 1; 2; 3...
...
=)
k n
1 1; 2; 3; : : :
2 2; 3; : : :
3 3; : : :...
...
It is
1Xn=k
1
n2=
1
k2+
1
(k + 1)2+
1
(k + 2)2+ : : :
� 1
k2+
1
k(k + 1)+
1
(k + 1)(k + 2)+ : : :
=1
k2+
1Xn=k+1
1
n(n� 1)
From Bronstein, page 30, # 7, we know that
1 =1
1 � 2 +1
2 � 3 +1
3 � 4 + : : :+1
n(n+ 1)+ : : :
=1
1 � 2 +1
2 � 3 +1
3 � 4 + : : :+1
(k � 1) � k +1X
n=k+1
1
n(n� 1)
=)1X
n=k+1
1
n(n� 1)= 1� 1
1 � 2 �1
2 � 3 �1
3 � 4 � : : : � 1
(k � 1) � k
=1
2� 1
2 � 3 �1
3 � 4 � : : :� 1
(k � 1) � k
=1
3� 1
3 � 4 � : : :� 1
(k � 1) � k
=1
4� : : :� 1
(k � 1) � k= : : :
=1
k
=)1Xn=k
1
n2� 1
k2+
1Xn=k+1
1
n(n� 1)
=1
k2+1
k
148
� 2
k
Using this result in (A), we get
1Xn=1
1
n2V ar(X 0
n) � 21Xk=1
(k + 1)2
kP (k �j X j< k + 1) + 2P (0 �j X j< 1)
= 21Xk=0
kP (k �j X j< k + 1) + 41Xk=1
P (k �j X j< k + 1)
+ 21Xk=1
1
kP (k �j X j< k + 1) + 2P (0 �j X j< 1)
(B)
� 2E(j X j) + 4 + 2 + 2
< 1
To establish (B), we use an inequality from the Proof of Lemma 6.3.11.
Thus, the conditions needed in Corollary 6.3.9 are met. It follows that
1
nT0n �
1
nE(T 0n)
a:s:�! 0 (C)
Since E(X 0n) ! E(X) as n! 1, it follows by Kronecker's Lemma (6.3.6) that 1
nE(T 0n) ! E(X).
Thus, when we replace 1nE(T
0n) by E(X) in (C), we get:
1
nT0n
a:s:�! E(X)Lemma 6:3:10
=) 1
nTn
a:s:�! E(X)
Merry Xmas and a Happy New Millennium!
149