Multilevel functional principal component analysis. (English) Zbl 1160.62061

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

Ann. Appl. Stat. 3, No. 1, 458-488 (2009).

Summary: 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.

MSC:

62H25	Factor analysis and principal components; correspondence analysis
92C20	Neural biology
62P10	Applications of statistics to biology and medical sciences; meta analysis

Keywords:

FPCA; multilevel models

Software:

SemiPar; fda (R)

References:

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