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arXiv · 1601.07443

Distribution of eigenvalues of sample covariance matrices with tensor product samples

Daria Tieplova
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Abstract

We consider $n^2\times n^2$ real symmetric and hermitian matrices $M_n$, which are equal to sum of $m_n$ tensor products of vectors $X^μ=B(Y^μ\otimes Y^μ)$, $μ=1,\dots,m_n$, where $Y^μ$ are i.i.d. random vectors from $\mathbb R^n (\mathbb C^n)$ with zero mean and unit variance of components, and $B$ is an $n^2\times n^2$ positive definite non-random matrix. We prove that if $m_n/n^2\to c\in [0,+\infty)$ and the Normalized Counting Measure of eigenvalues of $BJB$, where $J$ is defined below, converges weakly, then the Normalized Counting Measure of eigenvalues of $M_n$ converges weakly in probability to a non-random limit and its Stieltjes transform can be found from a certain functional equation.

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Daria Tieplova. 2016-05-26. Distribution of eigenvalues of sample covariance matrices with tensor product samples. https://arxiv.org/abs/1601.07443

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