Model selection in estimating equations

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Abstract

Model selection is a necessary step in many practical regression analyses. But for methods based on estimating equations, such as the quasi-likelihood and generalized estimating equation (GEE) approaches, there seem to be few well-studied model selection techniques. In this article, we propose a new model selection criterion that minimizes the expected predictive bias (EPB) of estimating equations. A bootstrap smoothed cross-validation (BCV) estimate of EPB is presented and its performance is assessed via simulation for overdispersed generalized linear models. For illustration, the method is applied to a real data set taken from a study of the development of ewe embryos.

Original languageEnglish (US)
Pages (from-to)529-534
Number of pages6
JournalBiometrics
Volume57
Issue number2
DOIs
StatePublished - Jun 2001

Keywords

  • Akaike information criterion
  • Bayesian information criterion
  • Bootstrap
  • Cross-validation
  • Generalized estimating equations
  • Generalized linear models
  • Quasi-likelihood

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