Information based model selection criteria for generalized linear mixed models with unknown variance component parameters

Authors:
Dalei Yu;Xinyu Zhang;Kelvin K. W. Yau
Affiliations:
Statistics and Mathematics College, Yunnan University of Finance and Economics, Kunming 650221, China;Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
Venue:
Journal of Multivariate Analysis
Year:
2013

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Abstract

This paper derives the corrected conditional Akaike information criteria for generalized linear mixed models by analytic approximation and parametric bootstrap. The sampling variation of both fixed effects and variance component parameter estimators are accommodated in the bias correction term. Simulation shows that the proposed corrected criteria provide good approximation to the true conditional Akaike information and demonstrates promising model selection results. The use of the criteria is demonstrated in the analysis of the chronic asthmatic patients' data.