Multilevel functional principal component analysis

Chong Zhi Di, Ciprian M. Crainiceanu, Brian S. Caffo, Naresh M. Punjabi

Research output: Contribution to journalArticlepeer-review

172 Scopus citations


The Sleep Heart Health Study (SHHS) is a comprehensive landmark study of sleep and its impacts on health outcomes. A primary metric of the SHHS is the in-home polysomnogram, which includes two electroencephalographic (EEG) channels for each subject, at two visits. The volume and importance of this data presents enormous challenges for analysis. To address these challenges, we introduce multilevel functional principal component analysis (MFPCA), a novel statistical methodology designed to extract core intra- and inter-subject geometric components of multilevel functional data. Though motivated by the SHHS, the proposed methodology is generally applicable, with potential relevance to many modern scientific studies of hierarchical or longitudinal functional outcomes. Notably, using MFPCA, we identify and quantify associations between EEG activity during sleep and adverse cardiovascular outcomes.

Original languageEnglish (US)
Pages (from-to)458-488
Number of pages31
JournalAnnals of Applied Statistics
Issue number1
StatePublished - Mar 2009


  • Functional principal component analysis (FPCA)
  • Multilevel models

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty


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