Variable selection for marginal longitudinal generalized linear models.
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).
Cantoni
Eva
E
Department of Econometrics, University of Geneva, CH-1211 Geneva 4, Switzerland. Eva.Cantoni@metri.unige.ch
Flemming
Joanna Mills
JM
Ronchetti
Elvezio
E
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