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...

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....

Under flexible distributional assumptions, the adjusted quasi-maximum likelihood (adqml) estimator for mixed regressive, spatial autoregressive model is studied in this paper. The proposed estimation method accommodates the extra uncertainty introduced by the unknown regression coefficients....

This note studies the existence and uniqueness of quasi-maximum likelihood estimator for mixed regressive, spatial autoregression model with continuously distributed response vector. Under very mild conditions that nrank(Xn)+1 (n is the sample size and Xn is the n×p constant matrix of...

This study presents three modeling techniques for the prediction of electricity energy consumption. In addition to the traditional regression analysis, decision tree and neural networks are considered. Model selection is based on the square root of average squared error. In an empirical...

When analyzing clustered count data derived from several latent subpopulations, the finite mixture of the Poisson mixed-effect model is an immediate strategy to model the underlying heterogeneity. Within the generalized linear mixed model framework, parameters in such a model are often estimated...