Modelling random effect variance with double hierarchical generalized linear models

被引：15
|
作者：
Lee, Youngjo [1 ]
Noh, Maengseok [2 ]
机构：
[1] Seoul Natl Univ, Dept Stat, Seoul 151742, South Korea
[2] Pukyong Natl Univ, Dept Stat, Pusan 608737, South Korea
来源：
STATISTICAL MODELLING | 2012年 / 12卷 / 06期
基金：
新加坡国家研究基金会;
关键词：
Double hierarchical generalized linear models; hierarchical generalized linear models; hierarchical likelihood; random effects; LIKELIHOOD RATIO TESTS; INFORMATION; INFERENCE;
D O I：
10.1177/1471082X12460132
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Random-effect models are becoming increasingly popular in the analysis of data. Lee and Nelder (2006) introduced double hierarchical generalized linear models (DHGLMs) in which not only the mean but also the residual variance (overdispersion) can be further modelled as random-effect models. In this article, we introduce DHGLMs that allow random-effect models for both the variances of random effects and the residual variance. We show how to use this general model class for the analysis of data and discuss how to select the best fitting model using the likelihood and various model-checking plots.
引用
收藏
页码：487 / 502
页数：16
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