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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
    2003
Published in:
  • Biometrics. - Blackwell Publishing. - 2005, vol. 61, no. 2, p. 507-514
Cp
generalized estimating equations (GEE)
prediction error
robustness
variable selection
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.
Language
  • English
Classification
Economics
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Open access status
green
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https://n2t.net/ark:/12658/srd1317880
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