English

Resolvent convergence for sample covariance matrices with general covariance profiles and quadratic-form control

Probability 2026-05-14 v4 Machine Learning

Abstract

We study the resolvent Gz=(1nXXTzIp)1,zC, (z)>0, G^z = \left(\frac{1}{n}XX^T - zI_p\right)^{-1}, \qquad z\in\mathbb C,\ \Im(z)>0, where X=(x1,,xn)Mp,nX=(x_1,\ldots,x_n)\in\mathcal M_{p,n} is a random matrix with independent, but not necessarily identically distributed, columns. Our bounds are expressed in terms of moments of the centered quadratic forms qi(A):=xiTAxiE[xiTAxi], q_i(A):=x_i^TAx_i-\mathbb E[x_i^TAx_i], for deterministic matrices AA with unit Hilbert--Schmidt norm. In particular, we do not assume independence between the entries of a given column xix_i. In the quasi-asymptotic regime pO(n)p\le O(n), the matrix GzG^z admits a natural deterministic equivalent G~z\tilde G^z, depending only on the second moments of the column vectors x1,,xnx_1,\ldots,x_n. We show that, for any deterministic matrix BMpB\in\mathcal M_p, the trace \tr(BGz)\tr(BG^z) is close to \tr(BG~z)\tr(B\tilde G^z), with error controlled by B\hs\|B\|_{\hs} under first-moment bounds on the quadratic forms, and by B\hs/n\|B\|_{\hs}/\sqrt n under suitable second-moment bounds.

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Cite

@article{arxiv.2109.02644,
  title  = {Resolvent convergence for sample covariance matrices with general covariance profiles and quadratic-form control},
  author = {Cosme Louart},
  journal= {arXiv preprint arXiv:2109.02644},
  year   = {2026}
}

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