Asymptotic properties of principal component projections with repeated eigenvalues

Justin Petrovich, Matthew Reimherr

Research output: Contribution to journalArticlepeer-review

3 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

Fingerprint

Dive into the research topics of 'Asymptotic properties of principal component projections with repeated eigenvalues'. Together they form a unique fingerprint.

Cite this