文章基本信息

标题：Statistical Inference in Generalized Linear Mixed Models by Joint Modelling Mean and Covariance of Non-Normal Random Effects
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作者：Yin Chen ; Yu Fei ; Jianxin Pan 等
期刊名称：Open Journal of Statistics
印刷版ISSN：2161-718X
电子版ISSN：2161-7198
出版年度：2015
卷号：05
期号：06
页码：568-584
DOI：10.4236/ojs.2015.56059
语种：English
出版社：Scientific Research Publishing
摘要：Generalized linear mixed models (GLMMs) are typically constructed by incorporating random effects into the linear predictor. The random effects are usually assumed to be normally distributed with mean zero and variance-covariance identity matrix. In this paper, we propose to release random effects to non-normal distributions and discuss how to model the mean and covariance structures in GLMMs simultaneously. Parameter estimation is solved by using Quasi-Monte Carlo (QMC) method through iterative Newton-Raphson (NR) algorithm very well in terms of accuracy and stabilization, which is demonstrated by real binary salamander mating data analysis and simulation studies.
关键词：Generalized Linear Mixed Models;Multivariate t Distribution;Multivariate Mixture Normal Distribution;Quasi-Monte Carlo;Newton-Raphson;Joint Modelling of Mean and Covariance