We study a class of separable sample covariance matrices of the form . Here, and 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 a spiked separable covariance matrix model. On the one hand, this model includes the spiked covariance matrix as a special case with . On the other hand, it allows for more general correlations of datasets. In particular, for spatio-temporal dataset, and 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 . We prove the convergence of the outlier eigenvalues and the generalized components (i.e., for any deterministic vector v) of the outlier eigenvectors 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 and will cause outliers of the eigenvalue spectrum, and the eigenvectors can help to select the outliers that correspond to the spikes of (or ).
"Spiked separable covariance matrices and principal components." Ann. Statist. 49 (2) 1113 - 1138, April 2021. https://doi.org/10.1214/20-AOS1995