THE SUBSET ARGUMENT AND CONSISTENCY OF MLE IN GLMM: ANSWER TO AN OPEN PROBLEM AND BEYOND

• 成果类型: Article
• 署名作者: Jiang, Jiming
• 署名单位: University of California System; University of California Davis
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/13-AOS1084
• 发表日期: 2013
• 页码: 177-195
• 关键词: linear mixed models; likelihood inference; conditional inference; bias correction; 2 components

摘要

We give answer to an open problem regarding consistency of the maximum likelihood estimators (MLEs) in generalized linear mixed models (GLMMs) involving crossed random effects. The solution to the open problem introduces an interesting, nonstandard approach to proving consistency of the MLEs in cases of dependent observations. Using the new technique, we extend the results to MLEs under a general GLMM. An example is used to further illustrate the technique.