摘要：The paper addresses the problem of selecting variables in linear mixed models (LMM)νll. We propose the Empirical Bayes Information Criterion (EBIC) using a partial prior information on the parameters of interest. Specifically EBIC incorporates a non-subjective prior distribution on regression coefficients with an unknown hyper-parameter, but it is free from the setup of a prior information on the nuisance parameters like variance components. It is shown that EBIC not only has the nice asymptotic property of consistency as a variable selection, but also performs better in small and large sample sizes than the conventional methods like AIC, conditional AIC and BIC in light of selecting true variables.
关键词：Akaike information criterion;Bayesian information criterion;consistency;empirical Bayes method;linear mixed model;maximum likelihood estimator;nested error regression model;random effect;restricted maximum likelihood estimator;selection of variables
