Structured functional principal component analysis

Haochang Shou, Vadim Zipunnikov, Ciprian M. Crainiceanu, Sonja Greven

Research output: Contribution to journalArticlepeer-review

Abstract

Motivated by modern observational studies, we introduce a class of functional models that expand nested and crossed designs. These models account for the natural inheritance of the correlation structures from sampling designs in studies where the fundamental unit is a function or image. Inference is based on functional quadratics and their relationship with the underlying covariance structure of the latent processes. A computationally fast and scalable estimation procedure is developed for high-dimensional data. Methods are used in applications including high-frequency accelerometer data for daily activity, pitch linguistic data for phonetic analysis, and EEG data for studying electrical brain activity during sleep.

Original languageEnglish (US)
Pages (from-to)247-257
Number of pages11
JournalBiometrics
Volume71
Issue number1
DOIs
StatePublished - Mar 1 2015

Keywords

  • Functional linear mixed model
  • Functional principal component analysis
  • Latent process
  • Multilevel correlation structure
  • Variance component

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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