Model comparison of generalized linear mixed models
Copyright policy )Model comparison of generalized linear mixed models
AbstractGeneralized 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. Copyright © 2005 John Wiley & Sons, Ltd.
- Chinese University of Hong Kong China (People's Republic of)
Likelihood Functions, Air Pollution, Linear Models, Humans, Bayes Theorem, Numerical Analysis, Computer-Assisted, Tobacco Smoke Pollution, Models, Theoretical, Child, Respiratory Tract Infections, Ohio
Likelihood Functions, Air Pollution, Linear Models, Humans, Bayes Theorem, Numerical Analysis, Computer-Assisted, Tobacco Smoke Pollution, Models, Theoretical, Child, Respiratory Tract Infections, Ohio
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