Statistical Inference in Generalized Linear Mixed Models by Joint Modelling Mean and Covariance of Non-Normal Random Effects

• 作者: Jianxin Pan Yin Chen Yu Fei
• 来源期刊: 统计学期刊(英文)
• 学科: 医学
• 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
• 年，卷（期）: 2015,（6）
• 页码范围: 568-584
• 页数: 17页
• 分类号: R73

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.