The Annals of Applied Statistics

Multilevel functional principal component analysis

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

Full-text: Open access

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.

Article information

Source
Ann. Appl. Stat. Volume 3, Number 1 (2009), 458-488.

Dates
First available in Project Euclid: 16 April 2009

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1239888378

Digital Object Identifier
doi:10.1214/08-AOAS206

Mathematical Reviews number (MathSciNet)
MR2668715

Zentralblatt MATH identifier
1160.62061

Keywords
Functional principal component analysis (FPCA) multilevel models

Citation

Di, Chong-Zhi; Crainiceanu, Ciprian M.; Caffo, Brian S.; Punjabi, Naresh M. Multilevel functional principal component analysis. Ann. Appl. Stat. 3 (2009), no. 1, 458--488. doi:10.1214/08-AOAS206. https://projecteuclid.org/euclid.aoas/1239888378


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