Information based model selection criteria for generalized linear mixed models with unknown variance component parameters

Abstract

This paper derives the corrected conditional Akaike information criteria for generalized linear mixed models by analytic approximation and parametric bootstrap. The sampling variation of both fixed effects and variance component parameter estimators are accommodated in the bias correction term. Simulation shows that the proposed corrected criteria provide good approximation to the true conditional Akaike information and demonstrates promising model selection results. The use of the criteria is demonstrated in the analysis of the chronic asthmatic patients’ data.