Variable selection for marginal longitudinal generalized linear models
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Cantoni, Eva
Department of Econometrics, University of Geneva, Switzerland
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Mills-Flemming, Joanna
Department of Econometrics, University of Geneva, Switzerland
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Ronchetti, Elvezio
Istituto di finanza (IFin), Facoltà di scienze economiche, Università della Svizzera italiana, Svizzera
Published in:
- 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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Economics
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License undefined
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https://n2t.net/ark:/12658/srd1317880
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