Asymptotic properties of principal component projections with repeated eigenvalues

Justin Petrovich, Matthew Reimherr

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

In FPCA methods, it is common to assume that the eigenvalues are distinct in order to facilitate theoretical proofs. We relax this assumption, provide a stochastic expansion for the estimated functional principal component projections, and establish their asymptotic normality.

Original languageEnglish (US)
Pages (from-to)42-48
Number of pages7
JournalStatistics and Probability Letters
Volume130
DOIs
StatePublished - Nov 2017

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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