Generalized multilevel functional regression

Ciprian M. Crainiceanu, Ana Maria Staicu, Chong Zhi Di

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1550-1561
Number of pages12
JournalJournal of the American Statistical Association
Volume104
Issue number488
DOIs
StatePublished - Dec 1 2009

    Fingerprint

Keywords

  • Functional principal components
  • Sleep EEG
  • Smoothing

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this