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May 2015 On the sample covariance matrix estimator of reduced effective rank population matrices, with applications to fPCA
Florentina Bunea, Luo Xiao
Bernoulli 21(2): 1200-1230 (May 2015). DOI: 10.3150/14-BEJ602


This work provides a unified analysis of the properties of the sample covariance matrix $\Sigma_{n}$ over the class of $p\times p$ population covariance matrices $\Sigma$ of reduced effective rank $r_{e}(\Sigma)$. This class includes scaled factor models and covariance matrices with decaying spectrum. We consider $r_{e}(\Sigma)$ as a measure of matrix complexity, and obtain sharp minimax rates on the operator and Frobenius norm of $\Sigma_{n}-\Sigma$, as a function of $r_{e}(\Sigma)$ and $\|\Sigma\|_{2}$, the operator norm of $\Sigma$. With guidelines offered by the optimal rates, we define classes of matrices of reduced effective rank over which $\Sigma_{n}$ is an accurate estimator. Within the framework of these classes, we perform a detailed finite sample theoretical analysis of the merits and limitations of the empirical scree plot procedure routinely used in PCA. We show that identifying jumps in the empirical spectrum that consistently estimate jumps in the spectrum of $\Sigma$ is not necessarily informative for other goals, for instance for the selection of those sample eigenvalues and eigenvectors that are consistent estimates of their population counterparts. The scree plot method can still be used for selecting consistent eigenvalues, for appropriate threshold levels. We provide a threshold construction and also give a rule for checking the consistency of the corresponding sample eigenvectors. We specialize these results and analysis to population covariance matrices with polynomially decaying spectra, and extend it to covariance operators with polynomially decaying spectra. An application to fPCA illustrates how our results can be used in functional data analysis.


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Florentina Bunea. Luo Xiao. "On the sample covariance matrix estimator of reduced effective rank population matrices, with applications to fPCA." Bernoulli 21 (2) 1200 - 1230, May 2015.


Published: May 2015
First available in Project Euclid: 21 April 2015

zbMATH: 06445972
MathSciNet: MR3338661
Digital Object Identifier: 10.3150/14-BEJ602

Rights: Copyright © 2015 Bernoulli Society for Mathematical Statistics and Probability


Vol.21 • No. 2 • May 2015
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