16
r TAB4 Stuff they don't tell you in regression Correlations and partial correlation The most commonly used regressions: Linear regression Simple/ multiple logistic regression (see GLM) Local (Curvilinear) Regression (e.g., LOESS, polynomial, splines, nonlinear) Ridge regression Lasso regression ElasticNet regression ANCOVA (see GLM) Non-parametric regression Kendall-Theil regression Spearman rank regression Rank regression Quantile regression Robust regression Ordinal regression Other types Principal component regression (PCR) Partial least square regression (PlS) Support vector regression Poisson, Beta and Negative binomial regression (see GLM) Quasi-Poisson regression Cox regression Bayesian regression LAD regression: (Similar to linear regression, but using abso lute values (Ll space)) Jackknife regression DISCL!\ IMEH ·1hcse note are for Dr. J. Dobel man' c; classes on mat he ma tica l and app lil'd s lal istics. Th c'>e notes arc nol intended tn be d islribuled ou lsidc the c l<1 ss r oo m se llin g. !'hey ar c based on a wide variety of so u recs. cfforl is 111,1dt> lli ensure that lhe co nte nt is co rrect in accor da nce with gene ra ll y aCCl'ptcd statistic.: il principlc'i and practices, bul no g uarantee can be mad e that inconsiste ncies might not be contained herein. Fo r a lm os t absolutely cor rect version-. ol these notes, please see the list of imp orta nt mon og r aphs va ri ously reported on o ur website or Canvas, or else '>Cc the r eso urces found in http://www.stat.rice.edu / -dobelma n/co urses/ statbooks . pdf .

TAB4 Stuff they don't tell you in regressiondobelman/courses/texts/Notes.411_616... · 2019. 4. 1. · Kendall-Theil regression Spearman rank regression Rank regression Quantile regression

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Page 1: TAB4 Stuff they don't tell you in regressiondobelman/courses/texts/Notes.411_616... · 2019. 4. 1. · Kendall-Theil regression Spearman rank regression Rank regression Quantile regression

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TAB4 Stuff they don't tell you in regression Correlations and partial correlation

The most commonly used regressions:

Linear regression

Simple/ multiple logistic regression (see GLM)

Local (Curvilinear) Regression (e.g., LOESS, polynomial, splines, nonlinear)

Ridge regression

Lasso regression

ElasticNet regression

ANCOV A (see GLM)

Non-parametric regression

Kendall-Theil regression

Spearman rank regression

Rank regression

Quantile regression

Robust regression

Ordinal regression

Other types

Principal component regression (PCR)

Partial least square regression (PlS)

Support vector regression

Poisson, Beta and Negative binomial regression (see GLM)

Quasi-Poisson regression

Cox regression

Bayesian regression

LAD regression: (Similar to linear regression, but using absolute values (Ll space))

Jackknife regression

DISCL!\ IMEH

·1 hcse note are for Dr. J. Dobelman' c; classes on mathematica l and applil'd slalistics. Thc'>e notes arc no l intended tn be d islribuled ou lsidc the c l<1ssroom selling. !'hey arc based on a wid e variety of sou recs. her~ cfforl is 111,1dt> lli ensure that lhe conte nt is correct in accorda nce with generally aCCl'ptcd statistic.:il principlc'i and practices, bul no guarantee can be mad e tha t inconsistencies might no t be contained herein. For a lmost absolutely correct version-. ol these no tes, please see the lis t of importa nt monographs va rious ly reported on our website or Canvas, or else '>Cc the resources found in http://www.stat.rice.edu/ -dobelman/courses / statbooks.pdf.

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