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Article
Multilevel Functional Principal Component Analysis
Annals of Applied Statistics (2009)
  • Chong-Zhi Di, Fred Hutchinson Cancer Research Center
  • Ciprian M Crainiceanu, Johns Hopkins University
  • Brian S Caffo, Johns Hopkins University
  • Naresh M Punjabi, Johns Hopkins University
Abstract

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.

Keywords
  • Functional principal component analysis (FPCA); multilevel models
Publication Date
2009
Citation Information
Chong-Zhi Di, Ciprian M Crainiceanu, Brian S Caffo and Naresh M Punjabi. "Multilevel Functional Principal Component Analysis" Annals of Applied Statistics Vol. 3 Iss. 1 (2009)
Available at: http://works.bepress.com/ciprian_crainiceanu/26/