Generalized Multilevel Functional Regression

• 成果类型: Article
• 署名作者: Crainiceanu, Ciprian M.; Staicu, Ana-Maria; Di, Chong-Zhi
• 署名单位: Johns Hopkins University; North Carolina State University; Fred Hutchinson Cancer Center
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.tm08564
• 发表日期: 2009
• 页码: 1550-1561
• 关键词: quasi-likelihood regression; variable selection; linear-models; ratio tests; distributions

摘要

We introduce Generalized Multilevel Functional Linear Models (GMFLMs), a novel statistical framework for regression models where exposure has a multilevel functional structure. We show that GMFLMs are, in fact, generalized multilevel mixed models. Thus, GMFLMs can be analyzed using the mixed effects inferential machinery and can be generalized within a well-researched statistical framework. We propose and compare two methods for inference: (1) a two-stage frequentist approach: and (2) a joint Bayesian analysis. Our methods are motivated by and applied to the Sleep Heart Health Study, the largest community cohort study of sleep. However, our methods are general and easy to apply to a wide spectrum of emerging biological and medical datasets. Supplemental materials for this article are available online.