Fast Multilevel Functional Principal Component Analysis

Erjia Cui, Ruonan Li, Ciprian M. Crainiceanu, Luo Xiao

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

We introduce fast multilevel functional principal component analysis (fast MFPCA), which scales up to high dimensional functional data measured at multiple visits. The new approach is orders of magnitude faster than and achieves comparable estimation accuracy with the original MFPCA. Methods are motivated by the National Health and Nutritional Examination Survey (NHANES), which contains minute-level physical activity information of more than 10, 000 participants over multiple days and 1440 observations per day. While MFPCA takes more than five days to analyze these data, fast MFPCA takes less than five minutes. A theoretical study of the proposed method is also provided. The associated function mfpca.face() is available in the R package refund. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)366-377
Number of pages12
JournalJournal of Computational and Graphical Statistics
Volume32
Issue number2
DOIs
StatePublished - 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.

Keywords

  • Functional principal component analysis
  • Mixed model equations
  • Multilevel models

PubMed: MeSH publication types

  • Journal Article

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