Upload
others
View
18
Download
0
Embed Size (px)
Citation preview
Normal Approximation from a Stein Identity(Based on joint work (in progress) with Aihua Xia)
Louis H. Y. Chen
National University of Singapore
Workshop on New Directions in Stein’s Method18 - 29 May 2015
Institute for Mathematical SciencesNational Universty of Singapore
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 1 / 29
Outline
Motivation
A General Theorem
Sums of Independent Random Variables
Random Measures
Completely Random Measures
Locally Dependent Random Measures
Maximal Points
Excursion Random Measures
Ginibre-Voronoi Tessellation
Stein Couplings
Summary
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 2 / 29
Motivation
Many problems in geometric probability can be formulated interms of random measures.
There is a Palm measure associated with each random measure -process version of size-biasness.
If we can couple the Palm measure to the random measure, wecan construct a Stein identity.
In this case, can we obtain a general Kolmogorov bound in thenormal approximation for random measures?
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 3 / 29
Motivation
Random measures and Palm measures
Γ a locally compact separable metric space.
Ξ a random measure on Γ with finite mean mesaure Λ, that is,Λ(A) = EΞ(A) for A ∈ B(Γ) and Λ(Γ) <∞.
Ξα the Palm measure associated with Ξ at α ∈ Γ, that is,
E(∫
Γ
f(α,Ξ)Ξ(dα)
)= E
(∫Γ
f(α,Ξα)Λ(dα)
). (1)
for real-valued functions f(·, ·) for which the expectations exist.
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 4 / 29
Motivation
Stein identity for random measures
By taking f to be absolutely continuous from R to R, (13)implies
E|Ξ|f(|Ξ|) = E∫
Γ
f(|Ξα|)Λ(dα). (2)
Let λ = Λ(Γ) = E|Ξ|, B2 = Var(|Ξ|), and define
W =|Ξ| − λB
, Wα =|Ξα| − λ
B.
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 5 / 29
Motivation
Stein identity for random measures (continued)
Assume that Ξ and Ξα, α ∈ Γ, are defined on the sameprobability space, and let
∆α = Wα −W.
From (2), we obtain
EWf(W ) = E∫ ∞−∞
f ′(W + t)K(t)dt
where
K(t) =1
B
∫Γ
[I(∆α > t > 0)− I(∆α < t ≤ 0)]Λ(dα).
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 6 / 29
Motivation
A general problem
Let W be such that EW = 0 and Var(W ) = 1. Suppose thereexists a random function K(t) such that
EWf(W ) = E∫ ∞−∞
f ′(W + t)K(t)dt (3)
for all absolutely continuous functions f for which theexpectations exist.
Can we obtain a meaningful bound on the Kolmogorov distancebetween L(W ) and N (0, 1) without further assumption?
Recall
dK(L(W ),N (0, 1)) := supx∈R|P (W ≤ x)− P (Z ≤ x)|,
Z ∼ N (0, 1).
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 7 / 29
A General Theorem
Theorem 1Let W be such that EW = 0 and Var(W ) = 1. Suppose there existsa random function K(t) such that
EWf(W ) = E∫ ∞−∞
f ′(W + t)K(t)dt
for all absolutely continuous functions f for which the expectationsexist.
Let K(t) = Kin(t) + Kout(t) where Kin(t) = 0 for |t| > 1. DefineK(t) = EK(t), Kin(t) = EKin(t), and Kout(t) = EKout(t).
Then
dK(L(W ),N (0, 1)) ≤ 2r1 + 12r2 + 8r3 + 9r4 + 15r5.
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 8 / 29
A General Theorem
In Theorem 1,
r1 =
[E(∫|t|≤1
(Kin(t)−Kin(t))dt
)2] 1
2
,
r2 =
∫|t|≤1
|tKin(t)|dt,
r3 = E∫ ∞−∞|Kout(t)|dt,
r4 = E∫|t|≤1
(Kin(t)−Kin(t))2dt,
r5 =
[E∫|t|≤1
|t|(Kin(t)−Kin(t))2dt
] 12
.
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 9 / 29
A General Theorem
Idea of proof
Adapt the techniques used in the proof of Theorem 2.1 (forLD1) in Chen and Shao (2004), Ann. Probab.
Instead of using a concentration inequality with explicit bound(whose proof is complicated), we use this inequality:For a ≤ b, a, b ∈ R,
P (a ≤ W ≤ b) ≤ 2dK(L(W ),N (0, 1)) +b− a√
2π.
Also use this inequality which plays a crucial role:For a, b, θ > 0,
ab ≤ θa2
2+b2
2θ.
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 10 / 29
A General Theorem
Idea of proof (continued)
E∫|t|≤1
I(x− 0 ∨ t ≤ W ≤ x− 0 ∧ t+ ε)|Kin(t)−Kin(t)|dt
≤ θβ
2E∫|t|≤1
I(x− 0 ∨ t ≤ W ≤ x− 0 ∧ t+ ε)
2d+ 0.4|t|+ 0.4εdt
+1
2θβE∫|t|≤1
(2d+ 0.4|t|+ 0.4ε)(Kin(t)−Kin(t))2dt
≤ θβ +d+ 0.2ε
θβr4 +
0.2
θβr2
5
≤ θd+ 0.2θε+1
θr4 + (θ +
0.2
θ)r5
(by letting β = d+ 0.2ε+ r5).
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 11 / 29
Sums of Independent Random Variables
What does the bound in Theorem 1 look like in the case of a sum ofindependent random variables?
Let ξ1, · · · , ξn be independent random variables with Eξi = 0,Var(ξi) = σ2
i , and E|ξi| <∞.
Let B2 =∑n
i=1 σ2i and let W =
1
B
n∑i=1
ξi.
Applying Theorem 1,
dK(L(W ),N (0, 1)) ≤17√∑n
i=1 Eξ4i I(|ξi| ≤ B)
B2+
21∑n
i=1 E|ξi|3
B3.
If both∑n
i=1 Eξ4i I(|ξi| ≤ B) and
∑ni=1 E|ξi|3 are O(B2), such
as in the i.i.d. case, then the bound is O(B−1), which agrees
with the order of the Berry-Esseen boundC∑n
i=1 E|ξi|3
B3.
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 12 / 29
Random Measures
Γ a locally compact separable metric space.
Ξ a random measure on Γ with finite mean mesaure Λ, that is,Λ(A) = EΞ(A) for A ∈ B(Γ) and Λ(Γ) <∞.
Ξα the Palm measure associated with Ξ at α ∈ Γ, that is,
E(∫
Γ
f(α,Ξ)Ξ(dα)
)= E
(∫Γ
f(α,Ξα)Λ(dα)
).
for real-valued functions f(·, ·) for which the expectations exist.
Assume that Ξ and Ξα, α ∈ Γ, are defined on the sameprobability space, and let ∆α = Wα −W .
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 13 / 29
Random Measures
We have
EWf(W ) = E∫ ∞−∞
f ′(W + t)K(t)dt
where
K(t) =1
B
∫Γ
χ(α, t)Λ(dα),
χ(α, t) = I(∆α > t > 0)− I(∆α < t ≤ 0).
Define
Kin(t) =1
B
∫Γ
χ(α, t)I(|∆α| ≤ 1)Λ(dα);
Kout(t) =1
B
∫Γ
χ(α, t)I(|∆α| > 1)Λ(dα).
Apply the main theorem (Theorem 1).
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 14 / 29
Completely Random Measures
A random measure Ξ on Γ is completely random ifΞ(A1), · · · ,Ξ(Ak) are independent wheneverA1, · · · , Ak ∈ B(Γ) are pairwise disjoint (Kingman (1967),Pacific J. Math.)
Examples are the compound Poisson process with clusterdistributions on R+ and the gamma process.
For the compound Poisson process and the gamma process,Γ = [0, T ].
These processes are not covered by the result of Barbour andXia (2006), Adv. Appl. Probab.
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 15 / 29
Completely Random Measures
Theorem 2Let Ξ be a completely random measure with mean measure Λ andfinite second moment E|Ξ|2 <∞. Let µ := µΞ = Λ(Γ),B2 = Var(|Ξ|) and let W = (|Ξ| − E|Ξ|)/B. Then
dK(L(W ),N (0, 1)) ≤ 13B−2ε1/22,Ξ + 12B−3ε1,Ξ + 9B−3ε3,Ξ,
where
ε1,Ξ :=
∫Γ
E(Ξ({α})2 + Ξα({α})2)Λ(dα),
ε2,Ξ :=∑
Γ
Λ({α})2E(Ξ({α})2 + Ξα({α})2),
ε3,Ξ :=∑
Γ
Λ({α})2E(Ξ({α}) + Ξα({α})).
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 16 / 29
Completely Random Measures
Corollary 3If the mean measure of the completely random measure is diffuse,that is, atomless, then
dK(L(W ),N (0, 1)) ≤ 12
B3ε1,Ξ.
Corollary 4
Let Ξ(i), 1 ≤ i ≤ n, be independent random measures on the carrier
space Γ and let Ξ =∑n
i=1 Ξ(i), B2 = Var(|Ξ|), W =|Ξ| − E|Ξ|
B.
Then
dK(L(W ),N (0, 1)) ≤ 19
B2
(n∑i=1
E|Ξ(i)|E|Ξ(i)|3)1/2
+42
B3
n∑i=1
E|Ξ(i)|3.
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 17 / 29
Locally Dependent Random Measures
A random measure Ξ on Γ with mean measure Λ is locallydependent with dependency neighborhoods Bα, α ∈ Γ, if
L(Ξα|Bcα) = L(Ξ|Bcα) Λ− a.s.
(Chen and Xia (2004), Ann. Probab.)
An example: Matern hard-core process - produced from aPoisson point process by deleting any point within distance r ofanother point, irrespective of whether the latter has itselfalready deleted.
Can couple Ξα to Ξ such that ΞBcα = ΞBcα , α ∈ Γ.
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 18 / 29
Locally Dependent Random Measures
Theorem 5Let Ξ be a random measure on Γ with finite mean measure Λ suchthat E|Ξ|4 <∞. Let B2 = Var(|Ξ|). Assume that Ξ and Ξα, α ∈ Γ,are defined on the same probability space. Define
W =|Ξ| − E|Ξ|
B, Wα =
|Ξα| − E|Ξ|B
, ∆α = Wα −W.
Assume that there is a set D ⊂ Γ× Γ such that for all (α, β) 6= D,∆α and ∆β are independent. Then
dK(L(W ),N (0, 1)) ≤ r1 + r2 + r3 + r4,
where
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 19 / 29
Locally Dependent Random Measures
r1 =1
B
[∫(α,β)∈D
E|∆α∆β|Λ(dα)Λ(dβ)
] 12
;
r2 =1
B
∫Γ
E∆2αΛ(dα);
r3 =1
B2
∫(α,β)∈D
EMin(|∆α|, |∆β|)Λ(dα)Λ(dβ);
r4 =1
B
[∫(α,β)∈D
EMin(∆2α,∆
2β)Λ(dα)Λ(dβ)
] 12
.
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 20 / 29
Maximal Points
P a Poisson point process with rate µ in the region:
D := {α = (α1, α2) : 0 ≤ α1 ≤ 1, 0 ≤ α2 ≤ F (α1)},F (0) = 1, F (1) = 0, F ′(x) < 0,
bounded away from 0 and −∞.
A point α ∈ D ∩ P is maximal if P ∩ Aα = {α}.
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 21 / 29
Maximal Points
Define Ξ(dα) = I(P ∩ Aα = {α})P (dα).
Ξ is a random measure on D and |Ξ| counts the total number ofmaximal points of the Poisson point process P in D.
Ξ is locally dependent.
Theorem 6
Let W =|Ξ| − E|Ξ|√
Var(|Ξ|. We have
dK(L(W ),N (0, 1)) = O
(log µ
µ1/4
)Bound same order as in Barbour and Xia (2006), Adv. Appl.Probab., but proof simpler - uses local dependence of only order1, that is, LD1.
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 22 / 29
Maximal Points
Idea of proof
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 23 / 29
Excursion Random Measures
S a metric space.
{Xt : 0 ≤ t ≤ T} an l-dependent S-valued random process, thatis, {Xt : 0 ≤ t ≤ a} and {Xt : b ≤ t ≤ T} are independent ifb− a > l.
Define the excursion random measure
Ξ(dt) = I((t,Xt) ∈ E)dt, E ∈ B([0, T ]× S)
Theorem 7
Let µ = EΞ([0, t]), B2 = Var(|Ξ|) and W =|Ξ| − µB
. We have
dK(L(W ),N (0, 1)) = O
(l3/2µ1/2
B2+l2µ
B3
).
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 24 / 29
Ginibre-Voronoi Tessellation
The Ginibre point process has attracted considerable attentionrecently because of its wide use in mobile networks and theGinibre-Voronoi tesselation.
The Ginibre point process is a special case of the Gibbs pointprocess family and it exhibits repulsion between points.
Goldman (2010), Ann. Appl. Probab. established that the Palmprocess Ξα associated with the Ginibre process Ξ at α ∈ Γsatisfies
L(Ξ) = L((Ξα \ {α}) ∪ {α + (Z1, Z2)}),
where
(Z1, Z2) ∼ N (0,1
2I2).
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 25 / 29
Ginibre-Voronoi Tessellation
Voronoi diagram (20 points and their Voronoi cells )
Define η(dα) = (Total edge length of cell containing α)Ξ(dα).
η is a locally dependent random measure.L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 26 / 29
Stein Couplings
Let (W,W ′, G) be a Stein coupling with Var(W ) = 1.
E{Gf(W ′)−Gf(W )} = E{Wf(W )} for all f for which theexpectations exist.
Let ∆ = W ′ −W and let F be a σ-algebra w.r.t. which W ismeasurable.
EWf(W ) = E∫∞−∞ f
′(W + t)K(t)dt, where
K(t) = E{G[I(∆ > t > 0)− I(∆ < t ≤ 0)]∣∣F}.
Kin(t) = E{G[I(∆ > t > 0)− I(∆ < t ≤ 0)]I(|∆| ≤ 1)∣∣F}.
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 27 / 29
Summary
We gave motivation and presented a general theorem for normalapproximation in Kolmogorov distance based on a Stein identity.
The theorem is applied to random measures, which includecompletely random measures and locally dependent randommeasures.
Explicit bounds are obtained for the number of maximal pointsin a Poisson point process and the excursion random measure ofan l-dependent random process.
We discussed briefly the random measure of total edge length ofcells in a Ginibre-Voronoi tessellation.
We also discussed briefly the construction of a Stein identity forStein couplings.
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 28 / 29
Thank You
L. H. Y. Chen (NUS) Normal approximation Stein’s method workshop 29 / 29