کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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415051 | 681162 | 2012 | 16 صفحه PDF | دانلود رایگان |

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.
Journal: Computational Statistics & Data Analysis - Volume 56, Issue 3, 1 March 2012, Pages 629–644