Sampled forms of functional PCA in reproducing kernel Hilbert spaces

Abstract

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.

Published in at http://dx.doi.org/10.1214/12-AOS1033 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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• Properties of principal component methods for functional and longitudinal data analysis
• Uniform convergence rates for nonparametric regression and principal component analysis in functional/longitudinal data
• High-dimensional analysis of semidefinite relaxations for sparse principal components
• Functional principal components analysis via penalized rank one approximation
• Augmented sparse principal component analysis for high dimensional data
• Approximation properties of certain operator-induced norms on Hilbert spaces

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