Central limit theorem for linear spectral statistics of general separable sample covariance matrices with applications

TL;DR

This paper proves a central limit theorem for linear spectral statistics of general separable sample covariance matrices, with applications to testing white noise in time series data.

Contribution

It establishes a new CLT for spectral statistics of a broad class of separable covariance matrices, extending previous results and enabling practical statistical tests.

Findings

Proved a CLT for spectral statistics of separable covariance matrices.

Applied the CLT to develop a test for white noise in time series.

Demonstrated the theoretical results with potential applications in wireless communications.

Abstract

In this paper, we consider the separable covariance model, which plays an important role in wireless communications and spatio-temporal statistics and describes a process where the time correlation does not depend on the spatial location and the spatial correlation does not depend on time. We established a central limit theorem for linear spectral statistics of general separable sample covariance matrices in the form of $\mathbf S_n=\frac1n\mathbf T_{1n}\mathbf X_n\mathbf T_{2n}\mathbf X_n^*\mathbf T_{1n}^*$ where $\mathbf X_n=(x_{jk})$ is of $m_1\times m_2$ dimension, the entries $\{x_{jk}, j=1,...,m_1, k=1,...,m_2\}$ are independent and identically distributed complex variables with zero means and unit variances, $\mathbf T_{1n}$ is a $p\times m_1 $ complex matrix and $\mathbf T_{2n}$ is an $m_2\times m_2$ Hermitian matrix. We then apply this general central limit theorem to the problem of testing white noise in time series.