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PubMed · 11414579

Model selection in estimating equations.

W Pan
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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.

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BiasBinomial DistributionBiometryComputer SimulationLikelihood FunctionsModels, StatisticalReproducibility of ResultsJournal Article

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W Pan. 2001. Model selection in estimating equations.. https://doi.org/10.1111/j.0006-341x.2001.00529.x

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