Open Access
October 2012 Sampled forms of functional PCA in reproducing kernel Hilbert spaces
Arash A. Amini, Martin J. Wainwright
Ann. Statist. 40(5): 2483-2510 (October 2012). DOI: 10.1214/12-AOS1033


We consider the sampling problem for functional PCA (fPCA), where the simplest example is the case of taking time samples of the underlying functional components. More generally, we model the sampling operation as a continuous linear map from $\mathcal{H}$ to $\mathbb{R}^{m}$, where the functional components to lie in some Hilbert subspace $\mathcal{H} $ of $L^{2}$, such as a reproducing kernel Hilbert space of smooth functions. This model includes time and frequency sampling as special cases. In contrast to classical approach in fPCA in which access to entire functions is assumed, having a limited number $m$ of functional samples places limitations on the performance of statistical procedures. We study these effects by analyzing the rate of convergence of an $M$-estimator for the subspace spanned by the leading components in a multi-spiked covariance model. The estimator takes the form of regularized PCA, and hence is computationally attractive. We analyze the behavior of this estimator within a nonasymptotic framework, and provide bounds that hold with high probability as a function of the number of statistical samples $n$ and the number of functional samples $m$. We also derive lower bounds showing that the rates obtained are minimax optimal.


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Arash A. Amini. Martin J. Wainwright. "Sampled forms of functional PCA in reproducing kernel Hilbert spaces." Ann. Statist. 40 (5) 2483 - 2510, October 2012.


Published: October 2012
First available in Project Euclid: 4 February 2013

zbMATH: 1373.62289
MathSciNet: MR3097610
Digital Object Identifier: 10.1214/12-AOS1033

Primary: 62G05
Secondary: 41A25 , 41A35 , 62H12 , 62H25

Keywords: Fourier truncation , functional principal component analysis , linear sampling operator , ‎reproducing kernel Hilbert ‎space , time sampling

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.40 • No. 5 • October 2012
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