title: Model comparison of generalized linear mixed models.
abstract: Generalized linear mixed models (GLMMs) have been widely appreciated in biological and medical research. Maximum likelihood estimation has received a great deal of attention. Comparatively, not much has been done on model comparison or hypotheses testing. In this article, we propose a path sampling procedure to compute the observed-data log-likelihood function, so that the Bayesian information criterion (BIC) can be applied to model comparison or hypothesis testing. Advantages of the proposed path sampling procedure are discussed. Two medical data sets are analysed for providing illustrative examples of the proposed methodology.
authors.affiliation: Department of Statistics, The Chinese University of Hong Kong, Hong Kong.
authors.forename: Xin-Yuan
authors.initials: XY
authors.lastname: Song
authors.forename: Sik-Yum
authors.initials: SY
authors.lastname: Lee
mesh_terms: D000397:Air Pollution / Q000009:adverse effects; D001499:Bayes Theorem; D002648:Child; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008962:Models, Theoretical; D009716:Numerical Analysis, Computer-Assisted; D009820:Ohio; D012141:Respiratory Tract Infections / Q000453:epidemiology; D014028:Tobacco Smoke Pollution / Q000009:adverse effects
chemical_list: D014028:Tobacco Smoke Pollution