Conditional Akaike information criterion for generalized linear mixed models

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

23 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)629-644
Journal / PublicationComputational Statistics and Data Analysis
Volume56
Issue number3
Publication statusPublished - 1 Mar 2012

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. © 2011 Elsevier B.V. All rights reserved.

Research Area(s)

  • Conditional Akaike information, Generalized linear mixed model, Model identification, Poisson time series, Variance component