Model comparison of generalized linear mixed models.

Xin-Yuan Song; Sik-Yum Lee

doi:10.1002/sim.2318

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PubMed · 16220521

Model comparison of generalized linear mixed models.

Xin-Yuan Song·Sik-Yum Lee

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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.

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Air Pollution Bayes Theorem Child Humans Likelihood Functions Linear Models Models, Theoretical Numerical Analysis, Computer-Assisted Ohio Respiratory Tract Infections Tobacco Smoke Pollution Comparative Study Journal Article Research Support, Non-U.S. Gov't

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Xin-Yuan Song, Sik-Yum Lee. 2006-05-30. Model comparison of generalized linear mixed models.. https://doi.org/10.1002/sim.2318

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