Journal
BIOMETRICS
Volume 61, Issue 2, Pages 507-514Publisher
BLACKWELL PUBLISHING
DOI: 10.1111/j.1541-0420.2005.00331.x
Keywords
Cp; generalized estimating equations (GEE); prediction error; robustness; variable selection
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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).
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