Asymptotic properties of principal component projections with repeated eigenvalues

Justin Petrovich and Matthew Reimherr

Statistics & Probability Letters, 2017, vol. 130, issue C, 42-48

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.

Keywords: Functional data analysis; Repeated eigenvalues; Principal components; Projection (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.spl.2017.07.004

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