Conditional Akaike information criterion for generalized linear mixed models

Authors:
Dalei Yu;Kelvin K. W. Yau
Affiliations:
Statistics and Mathematics College, Yunnan University of Finance and Economics, Kunming 650221, China and Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, ...;Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
Venue:
Computational Statistics & Data Analysis
Year:
2012

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Abstract

In this study, a model identification instrument to determine the variance component structure for generalized linear mixed models (glmms) is developed based on the conditional Akaike information (cai). In particular, an asymptotically unbiased estimator of the cai (denoted as caicc) is derived as the model selection criterion which takes the estimation uncertainty in the variance component parameters into consideration. The relationship between bias correction and generalized degree of freedom for glmms is also explored. Simulation results show that the estimator performs well. The proposed criterion demonstrates a high proportion of correct model identification for glmms. Two sets of real data (epilepsy seizure count data and polio incidence data) are used to illustrate the proposed model identification method.