On the rate of convergence in the CLT for LSS of large-dimensional sample covariance matrices

TL;DR

This paper establishes the rate at which the linear spectral statistic of large-dimensional sample covariance matrices converges to a normal distribution, using Stein's method under certain moment and ratio conditions.

Contribution

It provides a quantitative convergence rate for the CLT of LSS of large covariance matrices, extending previous asymptotic results with explicit bounds.

Findings

Convergence rate is O(n^{-1/2+κ}) in Kolmogorov-Smirnov distance.

Requires finite 10th moment of matrix entries.

Applicable when p/n approaches a positive constant y.

Abstract

This paper investigates the rate of convergence for the central limit theorem of linear spectral statistic (LSS) associated with large-dimensional sample covariance matrices. We consider matrices of the form ${\mathbf B}_n=\frac{1}{n}{\mathbf T}_p^{1/2}{\mathbf X}_n{\mathbf X}_n^*{\mathbf T}_p^{1/2},$ where ${\mathbf X}_n= (x_{i j} ) $ is a $p \times n$ matrix whose entries are independent and identically distributed (i.i.d.) real or complex variables, and ${\mathbf T} _p$ is a $p\times p$ nonrandom Hermitian nonnegative definite matrix with its spectral norm uniformly bounded in $p$. Employing Stein's method, we establish that if the entries $x_{ij}$ satisfy $\mathbb{E}|x_{ij}|^{10}<\infty$ and the ratio of the dimension to sample size $p/n\to y>0$ as $n\to\infty$, then the convergence rate of the normalized LSS of ${\mathbf B}_n$ to the standard normal distribution, measured in the Kolmogorov-Smirnov distance, is $O(n^{-1/2+\kappa})$ for any fixed $\kappa>0$.