Two-sample test for high-dimensional covariance matrices: A normal-reference approach

Jingyi Wang; Tianming Zhu; Jin‐Ting Zhang

doi:10.1016/j.jmva.2024.105354

ScienceGate Book Chapters

JOURNAL ARTICLE

Two-sample test for high-dimensional covariance matrices: A normal-reference approach

Jingyi Wang Tianming Zhu Jin‐Ting Zhang

Year: 2024 Journal: Journal of Multivariate Analysis Vol: 204 Pages: 105354-105354 Publisher: Elsevier BV

DOI: 10.1016/j.jmva.2024.105354

Get Full-Text PDF Get Analytical Report

Keywords:

Mathematics Test statistic Chi-square test Null distribution Statistics Pearson's chi-squared test Statistical hypothesis testing Covariance Asymptotic distribution Type I and type II errors Null hypothesis Normal distribution Applied mathematics Z-test Sample size determination Inference Type (biology) Statistic

Metrics

Cited By

6.13

FWCI (Field Weighted Citation Impact)

Refs

0.92

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Random Matrices and Applications

Physical Sciences → Mathematics → Statistics and Probability

Bayesian Methods and Mixture Models

Physical Sciences → Computer Science → Artificial Intelligence

Statistical Methods and Bayesian Inference

Physical Sciences → Mathematics → Statistics and Probability

Two-sample test for high-dimensional covariance matrices: A normal-reference approach

Metrics

Citation History

Topics

Related Documents

GLHT for High-Dimensional Covariance Matrices: A Normal-Reference Approach

GLHT for High-Dimensional Covariance Matrices: A Normal-Reference Approach

GLHT for High-Dimensional Covariance Matrices: A Normal-Reference Approach

GLHT for high-dimensional covariance matrices: A normal-reference approach*

Test of the Equality of Several High-Dimensional Covariance Matrices: A Normal-Reference Approach