Information Based Model Selection Criterion for Binary Response Generalized Linear Mixed Models

Conditional Akaike information criterion is derived within the framework of conditional-likelihood-based method for binary response generalized linear mixed models. The criterion essentially is the asymptotically unbiased estimator of conditional Akaike information based on maximum likelihood estimator. The proposed criterion is adopted to address the model selection problems in binary response generalized linear mixed models. Comparing with other Monte-Carlo EM based methods, conditional Akaike information criterion is more flexible and computationally attractive. Simulations show that the performance of the proposed criterion is in general promising. The use of the criterion is demonstrated in the analysis of the chronic asthmatic patients data.