Model selection in estimating equations.
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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Division of Biostatistics, University of Minnesota, Minneapolis 55455, USA. weip@biostat.umn.edu
D015982:Bias; D016010:Binomial Distribution; D001699:Biometry / Q000379:methods; D003198:Computer Simulation; D016013:Likelihood Functions; D015233:Models, Statistical; D015203:Reproducibility of Results