JSM 2004 - Toronto

Abstract #301102

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Activity Number: 224
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #301102
Title: Variable Selection for Marginal Longitudinal Generalized Linear Models
Author(s): Eva Cantoni*+ and Joanna Mills Flemming and Elvezio Ronchetti
Companies: University of Geneva and University of Geneva and University of Geneva
Address: Econometrics Dept., GENEVA, 1205, Switzerland
Keywords: Cp ; generalized estimating equations (GEE) ; prediction error ; robustness ; variable selection
Abstract:

Variable selection is an essential part of any statistical analysis and yet has been somewhat neglected in the context of longitudinal data analysis. We propose a generalized version of Mallows's Cp (GCp) suitable for use with both parametric and nonparametric models. GCp provides an estimate of a measure of adequacy of a model for prediction. We examine its performance with popular marginal longitudinal models (fitted using GEE) and contrast results with what is typically done in practice: variable selection based on Wald-type tests. An application to real data further demonstrates the merits of our approach while at the same time emphasizing some important robust features inherent to GCp.

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