Article ID Journal Published Year Pages File Type
415051 Computational Statistics & Data Analysis 2012 16 Pages PDF
Abstract

In this study, a model identification instrument to determine the variance component structure for generalized linear mixed models (glmmglmms) is developed based on the conditional Akaike information (caicai). In particular, an asymptotically unbiased estimator of the caicai (denoted as caicccaicc) 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 glmmglmms is also explored. Simulation results show that the estimator performs well. The proposed criterion demonstrates a high proportion of correct model identification for glmmglmms. Two sets of real data (epilepsy seizure count data and polio incidence data) are used to illustrate the proposed model identification method.

Related Topics
Physical Sciences and Engineering Computer Science Computational Theory and Mathematics
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