A note on conditional aic for linear mixed-effects models

Abstract

The conventional model selection criterion, the Akaike information criterion, aic , has been applied to choose candidate models in mixed-effects models by the consideration of marginal likelihood. Vaida & Blanchard (2005) demonstrated that such a marginal aic and its small sample correction are inappropriate when the research focus is on clusters. Correspondingly, these authors suggested the use of conditional aic . Their conditional aic is derived under the assumption that the variance-covariance matrix or scaled variance-covariance matrix of random effects is known. This note provides a general conditional aic but without these strong assumptions. Simulation studies show that the proposed method is promising.