Hypergeometric functions of matrix arguments and linear statistics of multi-spiked Hermitian matrix models

Damien Passemier, Matthew R. McKay and Yang Chen

Journal of Multivariate Analysis, 2015, vol. 139, issue C, 124-146

Abstract: This paper derives central limit theorems (CLTs) for general linear spectral statistics (LSS) of three important multi-spiked Hermitian random matrix ensembles. The first is the most common spiked scenario, proposed by Johnstone, which is a central Wishart ensemble with fixed-rank perturbation of the identity matrix, the second is a non-central Wishart ensemble with fixed-rank noncentrality parameter, and the third is a similarly defined non-central F ensemble. These CLT results generalize our recent work Passemier (2015) to account for multiple spikes, which is the most common scenario met in practice. The generalization is non-trivial, as it now requires dealing with hypergeometric functions of matrix arguments. To facilitate our analysis, for a broad class of such functions, we first generalize a recent result of Onatski (2014) to present new contour integral representations, which are particularly suitable for computing large-dimensional properties of spiked matrix ensembles. Armed with such representations, our CLT formulas are derived for each of the three spiked models of interest by employing the Coulomb fluid method from random matrix theory along with saddlepoint techniques. We find that for each matrix model, and for general LSS, the individual spikes contribute additively to yield a O(1) correction term to the asymptotic mean of the linear statistic, which we specify explicitly, whilst having no effect on the leading order terms of the mean or variance.

Keywords: Random matrix theory; High-dimensional statistics; Spiked population model; Hypergeometric function; Wishart ensembles; F-matrix (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1016/j.jmva.2015.03.001

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