Model comparison with composite likelihood information criteria

Subscribe to email list

Please select the email list(s) to which you wish to subscribe.

User menu

You are here

Model comparison with composite likelihood information criteria

TitleModel comparison with composite likelihood information criteria
Publication TypeJournal Article
Year of Publication2014
AuthorsNg, CT, Joe, H
Date PublishedNOV
Type of ArticleArticle
KeywordsAkaike information criterion, Bayesian information criterion, local alternatives, mixed-effects model, Model comparison
AbstractComparisons are made for the amount of agreement of the composite likelihood information criteria and their full likelihood counterparts when making decisions among the fits of different models; and some properties of penalty term for composite likelihood information criteria are obtained. Asymptotic theory is given for the case when a simpler model is nested within a bigger model, and the bigger model approaches the simpler model under a sequence of local alternatives. Composite likelihood can more or less frequently choose the bigger model, depending on the direction of local alternatives; in the former case, composite likelihood has more ``power'' to choose the bigger model. The behaviors of the information criteria are illustrated via theory and simulation examples of the Gaussian linear mixed-effects model.