Analysis of the limiting spectral measure of large random matrices of the separable covariance type

Abstract

Consider the random matrix $\Sigma =D^{1/2}X{\tilde{D}}^{1/2}$ where $D$ and $\tilde{D}$ are deterministic Hermitian nonnegative matrices with respective dimensions $N\times N$ and $n\times n$, and where $X$ is a random matrix with independent and identically distributed centered elements with variance $1/n$. Assume that the dimensions $N$ and $n$ grow to infinity at the same pace, and that the spectral measures of $D$ and $\tilde{D}$ converge as $N,n\to \infty$ towards two probability measures. Then it is known that the spectral measure of $\Sigma {\Sigma }^{\ast }$ converges towards a probability measure $\mu$ characterized by its Stieltjes Transform. In this paper, it is shown that $\mu$ has a density away from zero, this density is analytical wherever it is positive, and it behaves in most cases as $\sqrt{|x-a|}$ near an edge $a$ of its support. A complete characterization of the support of $\mu$ is also provided. \\ Beside its mathematical interest, this analysis finds applications in a certain class of statistical estimation problems.

Correction of the proof of Lemma 3.3

References in corpus (1)

• Improved subspace estimation for multivariate observations of high dimension: the deterministic signals case