April 2021 Spiked separable covariance matrices and principal components
Xiucai Ding, Fan Yang
Author Affiliations +
Ann. Statist. 49(2): 1113-1138 (April 2021). DOI: 10.1214/20-AOS1995


We study a class of separable sample covariance matrices of the form Q˜1:=A˜1/2XB˜XA˜1/2. Here, A˜ and B˜ are positive definite matrices whose spectrums consist of bulk spectrums plus several spikes, that is, larger eigenvalues that are separated from the bulks. Conceptually, we call Q˜1 a spiked separable covariance matrix model. On the one hand, this model includes the spiked covariance matrix as a special case with B˜=I. On the other hand, it allows for more general correlations of datasets. In particular, for spatio-temporal dataset, A˜ and B˜ represent the spatial and temporal correlations, respectively.

In this paper, we study the outlier eigenvalues and eigenvectors, that is, the principal components, of the spiked separable covariance model Q˜1. We prove the convergence of the outlier eigenvalues λ˜i and the generalized components (i.e., v,ξ˜i for any deterministic vector v) of the outlier eigenvectors ξ˜i with optimal convergence rates. Moreover, we also prove the delocalization of the nonoutlier eigenvectors. We state our results in full generality, in the sense that they also hold near the so-called BBP transition and for degenerate outliers. Our results highlight both the similarity and difference between the spiked separable covariance matrix model and the spiked covariance matrix model in (Probab. Theory Related Fields 164 (2016) 459–552). In particular, we show that the spikes of both A˜ and B˜ will cause outliers of the eigenvalue spectrum, and the eigenvectors can help to select the outliers that correspond to the spikes of A˜ (or B˜).


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Xiucai Ding. Fan Yang. "Spiked separable covariance matrices and principal components." Ann. Statist. 49 (2) 1113 - 1138, April 2021. https://doi.org/10.1214/20-AOS1995


Received: 1 July 2019; Revised: 1 June 2020; Published: April 2021
First available in Project Euclid: 2 April 2021

Digital Object Identifier: 10.1214/20-AOS1995

Primary: 15B52 , 62E20
Secondary: 62H99

Keywords: BBP transition , local laws , principal components , Spiked separable covariance matrices

Rights: Copyright © 2021 Institute of Mathematical Statistics


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Vol.49 • No. 2 • April 2021
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