Asymptotic normality of the principal components of functional time series

Piotr Kokoszka, Matthew Reimherr

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

We establish the asymptotic normality of the sample principal components of functional stochastic processes under nonrestrictive assumptions which admit nonlinear functional time series models. We show that the aforementioned asymptotic depends only on the asymptotic normality of the sample covariance operator, and that the latter condition holds for weakly dependent functional time series which admit expansions as Bernoulli shifts. The weak dependence is quantified by the condition of L4-m-approximability which includes all functional time series models in practical use. We also demonstrate convergence of the cross covariance operators of the sample functional principal components to their counterparts in the normal limit.

Original languageEnglish (US)
Pages (from-to)1546-1562
Number of pages17
JournalStochastic Processes and their Applications
Volume123
Issue number5
DOIs
StatePublished - 2013

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

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