Conditional inference about generalized linear mixed models

• 成果类型: Article
• 署名作者: Jiang, JM
• 署名单位: University System of Ohio; Case Western Reserve University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1017939247
• 发表日期: 1999
• 页码: 1974-2007
• 关键词: Approximation; estimators; components

摘要

We propose a method of inference for generalized linear mixed models (GLMM) that in many ways resembles the method of least squares. We also show that adequate inference about GLMM can be made based on the conditional likelihood on a subset of the random effects. One of the important features of our methods is that they rely on weak distributional assumptions about the random effects. The methods proposed are also computationally feasible. Asymptotic behavior of the estimates is investigated. In particular, consistency is proved under reasonable conditions.