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Asymptotic properties of principal component projections with repeated eigenvalues

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  • Petrovich, Justin
  • Reimherr, Matthew

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.

Suggested Citation

  • Petrovich, Justin & Reimherr, Matthew, 2017. "Asymptotic properties of principal component projections with repeated eigenvalues," Statistics & Probability Letters, Elsevier, vol. 130(C), pages 42-48.
    • Handle: RePEc:eee:stapro:v:130:y:2017:i:c:p:42-48
    DOI: 10.1016/j.spl.2017.07.004
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    Cited by:

    1. Ruiz-Medina, M.D. & Álvarez-Liébana, J., 2019. "A note on strong-consistency of componentwise ARH(1) predictors," Statistics & Probability Letters, Elsevier, vol. 145(C), pages 224-228.

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