A CLT for the LSS of large dimensional sample covariance matrices with diverging spikes

TL;DR

This paper extends the central limit theorem for linear spectral statistics of large sample covariance matrices to include diverging spikes in population covariance, enabling more accurate high-dimensional statistical inference.

Contribution

It introduces a new CLT that accounts for diverging spikes in population covariance matrices, broadening the applicability of the Bai-Silverstein theorem in high-dimensional settings.

Findings

The new CLT accommodates both bounded and diverging spikes.

The variance depends on spiked and bulk eigenvalues, influenced by divergence rate.

LSS-based tests can outperform Roy's largest root test in power.

Abstract

In this paper, we establish the central limit theorem (CLT) for linear spectral statistics (LSSs) of a large-dimensional sample covariance matrix when the population covariance matrices are involved with diverging spikes. This constitutes a nontrivial extension of the Bai-Silverstein theorem (BST) (Ann Probab 32(1):553--605, 2004), a theorem that has strongly influenced the development of high-dimensional statistics, especially in the applications of random matrix theory to statistics. Recently, there has been a growing realization that the assumption of uniform boundedness of the population covariance matrices in the BST is not satisfied in some fields, such as economics, where the variances of principal components may diverge as the dimension tends to infinity. Therefore, in this paper, we aim to eliminate this obstacle to applications of the BST. Our new CLT accommodates spiked eigenvalues, which may either be bounded or tend to infinity. A distinguishing feature of our result is that the variance in the new CLT is related to both spiked eigenvalues and bulk eigenvalues, with dominance being determined by the divergence rate of the largest spiked eigenvalues. The new CLT for LSS is then applied to test the hypothesis that the population covariance matrix is the identity matrix or a generalized spiked model. The asymptotic distributions of the corrected likelihood ratio test statistic and the corrected Nagao's trace test statistic are derived under the alternative hypothesis. Moreover, we present power comparisons between these two LSSs and Roy's largest root test. In particular, we demonstrate that except for the case in which the number of spikes is equal to one, the LSSs could exhibit higher asymptotic power than Roy's largest root test.