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March 2009 Multilevel functional principal component analysis
Chong-Zhi Di, Ciprian M. Crainiceanu, Brian S. Caffo, Naresh M. Punjabi
Ann. Appl. Stat. 3(1): 458-488 (March 2009). DOI: 10.1214/08-AOAS206


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.


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Chong-Zhi Di. Ciprian M. Crainiceanu. Brian S. Caffo. Naresh M. Punjabi. "Multilevel functional principal component analysis." Ann. Appl. Stat. 3 (1) 458 - 488, March 2009.


Published: March 2009
First available in Project Euclid: 16 April 2009

zbMATH: 1160.62061
MathSciNet: MR2668715
Digital Object Identifier: 10.1214/08-AOAS206

Keywords: Functional Principal Component Analysis (FPCA) , multilevel models

Rights: Copyright © 2009 Institute of Mathematical Statistics


Vol.3 • No. 1 • March 2009
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