Open Access
August 2019 Central limit theorem for linear spectral statistics of large dimensional separable sample covariance matrices
Zhidong Bai, Huiqin Li, Guangming Pan
Bernoulli 25(3): 1838-1869 (August 2019). DOI: 10.3150/18-BEJ1038

Abstract

Suppose that $\mathbf{X}_{n}=(x_{jk})$ is $N\times n$ whose elements are independent complex variables with mean zero, variance 1. The separable sample covariance matrix is defined as $\mathbf{B}_{n}=\frac{1}{N}\mathbf{T}_{2n}^{1/2}\mathbf{X}_{n}\mathbf{T}_{1n}\mathbf{X}_{n}^{*}\mathbf{T}_{2n}^{1/2}$ where $\mathbf{T}_{1n}$ is a Hermitian matrix and $\mathbf{T}_{2n}^{1/2}$ is a Hermitian square root of the nonnegative definite Hermitian matrix $\mathbf{T}_{2n}$. Its linear spectral statistics (LSS) are shown to have Gaussian limits when $n/N$ approaches a positive constant under some conditions.

Citation

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Zhidong Bai. Huiqin Li. Guangming Pan. "Central limit theorem for linear spectral statistics of large dimensional separable sample covariance matrices." Bernoulli 25 (3) 1838 - 1869, August 2019. https://doi.org/10.3150/18-BEJ1038

Information

Received: 1 July 2017; Revised: 1 January 2018; Published: August 2019
First available in Project Euclid: 12 June 2019

zbMATH: 07066242
MathSciNet: MR3961233
Digital Object Identifier: 10.3150/18-BEJ1038

Keywords: central limit theorem , Linear spectral statistics , Random matrix theory , separable sample covariance matrix

Rights: Copyright © 2019 Bernoulli Society for Mathematical Statistics and Probability

Vol.25 • No. 3 • August 2019
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