High-dimensional covariance matrices in elliptical distributions with application to spherical test

03/21/2018
by   Jiang Hu, et al.
0

This paper discusses fluctuations of linear spectral statistics of high-dimensional sample covariance matrices when the underlying population follows an elliptical distribution. Such population often possesses high order correlations among their coordinates, which have great impact on the asymptotic behaviors of linear spectral statistics. Taking such kind of dependency into consideration, we establish a new central limit theorem for the linear spectral statistics in this paper for a class of elliptical populations. This general theoretical result has wide applications and, as an example, it is then applied to test the sphericity of elliptical populations.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/23/2020

Asymptotic independence of spiked eigenvalues and linear spectral statistics for large sample covariance matrices

We consider general high-dimensional spiked sample covariance models and...
research
12/12/2022

A CLT for the LSS of large dimensional sample covariance matrices with diverging spikes

In this paper, we establish the central limit theorem (CLT) for linear s...
research
12/13/2018

Numerical techniques for the computation of sample spectral distributions of population mixtures

This note describes some techniques developed for the computation of the...
research
09/08/2022

A Bootstrap Method for Spectral Statistics in High-Dimensional Elliptical Models

Although there is an extensive literature on the eigenvalues of high-dim...
research
01/22/2022

Estimation of the covariance structure from SNP allele frequencies

We propose two new statistics, V and S, to disentangle the population hi...
research
01/02/2020

Modified Pillai's trace statistics for two high-dimensional sample covariance matrices

The goal of this study was to test the equality of two covariance matric...
research
05/10/2018

Wald Statistics in high-dimensional PCA

In this note we consider PCA for Gaussian observations X_1,..., X_n with...

Please sign up or login with your details

Forgot password? Click here to reset