title: Variable selection for marginal longitudinal generalized linear models.
abstract: Variable selection is an essential part of any statistical analysis and yet has been somewhat neglected in the context of longitudinal data analysis. In this article, we propose a generalized version of Mallows's C(p) (GC(p)) suitable for use with both parametric and nonparametric models. GC(p) provides an estimate of a measure of model's adequacy 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 or score-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 GC(p).
authors: Department of Econometrics, University of Geneva, CH-1211 Geneva 4, Switzerland. Eva.Cantoni@metri.unige.ch
authors: Eva
authors: E
authors: Cantoni
authors: Joanna Mills
authors: JM
authors: Flemming
authors: Elvezio
authors: E
authors: Ronchetti
mesh_terms: D015982:Bias; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D012044:Regression Analysis; D012107:Research Design; D018709:Statistics, Nonparametric