Journal article

Variable selection for marginal longitudinal generalized linear models

  • Cantoni, Eva Department of Econometrics, University of Geneva, Switzerland
  • Mills-Flemming, Joanna Department of Econometrics, University of Geneva, Switzerland
  • Ronchetti, Elvezio Istituto di finanza (IFin), Facoltà di scienze economiche, Università della Svizzera italiana, Svizzera
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  • Biometrics. - Blackwell Publishing. - 2005, vol. 61, no. 2, p. 507-514
English 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 paper 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 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 GCp.
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