Large sample correlation matrices: a comparison theorem and its applications

Abstract

In this paper, we show that the diagonal of a high-dimensional sample covariance matrix stemming from n independent observations of a p-dimensional time series with finite fourth moments can be approximated in spectral norm by the diagonal of the population covariance matrix. We assume that $n,p\to \mathrm{\infty }$ with $p∕n$ tending to a constant which might be positive or zero. As applications, we provide an approximation of the sample correlation matrix R and derive a variety of results for its eigenvalues. We identify the limiting spectral distribution of R and construct an estimator for the population correlation matrix and its eigenvalues. Finally, the almost sure limits of the extreme eigenvalues of R in a generalized spiked correlation model are analyzed.