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Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Automatic variable selection for longitudinal generalized linear models

Language

English

Abstract

We consider the problem of variable selection for the generalized linear models (GLMs) with longitudinal data. An automatic variable selection procedure is developed using smooth-threshold generalized estimating equations (SGEE). The proposed procedure automatically eliminates inactive predictors by setting the corresponding parameters to be zero, and simultaneously estimates the nonzero regression coefficients by solving the SGEE. The proposed method shares some of the desired features of existing variable selection methods: the resulting estimator enjoys the oracle property; the proposed procedure avoids the convex optimization problem and is flexible and easy to implement. Moreover, we propose a penalized weighted deviance criterion for a data-driven choice of the tuning parameters. Simulation studies are carried out to assess the performance of SGEE, and a real dataset is analyzed for further illustration. © 2012 Elsevier B.V. All rights reserved.

Keywords

Automatic variable selection, Generalized estimating equations, Generalized linear model, Longitudinal data, Oracle property

Publication Date

2013

Source Publication Title

Computational Statistics & Data Analysis

Volume

61

Start Page

174

End Page

186

Publisher

Elsevier

ISSN (print)

01679473

ISSN (electronic)

18727352

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