Hypothesis testing for the identity of high-dimensional covariance matrices

被引：1

作者：

Qian, Manling ^{[1
]}

Tao, Li ^{[1
]}

Li, Erqian ^{[1
]}

Tian, Maozai ^{[1
,2
,3
]}

机构：

[1] Renmin Univ China, Ctr Appl Stat, Sch Stat, Beijing 100872, Peoples R China

[2] Xinjiang Univ Finance & Econ, Sch Stat & Informat, Urumqi 830012, Xinjiang, Peoples R China

[3] Lanzhou Univ Finance & Econ, Sch Stat, Lanzhou 730101, Gansu, Peoples R China

来源：

STATISTICS & PROBABILITY LETTERS | 2020年 / 161卷

关键词：

Covariance matrix; High-dimensional data; Hypothesis testing; Identity matrix; SPHERICITY;

D O I：

10.1016/j.spl.2020.108699

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

A new test statistic is proposed by utilizing the eigenvalues of the sample covariance matrix for the identity test. Under some general assumptions, asymptotic distributions of the proposed test statistic T and tests proposed in previous literature (denoted as T-s, T-1,T-2) are given. Simulations are also conducted to evaluate their performance in a finite sample. (C) 2020 Elsevier B.V. All rights reserved.

引用

页数：12

共 50 条

[41] Hypothesis Testing for Independence given a Blocked Compound Symmetric Covariance Structure in a High-Dimensional Setting
Shin-ichi Tsukada
[J]. Journal of Statistical Theory and Practice, 2023, 17
[42] Testing and support recovery of multiple high-dimensional covariance matrices with false discovery rate control
Xia, Yin
[J]. TEST, 2017, 26 (04) : 782 - 801
[43] Testing and support recovery of multiple high-dimensional covariance matrices with false discovery rate control
Yin Xia
[J]. TEST, 2017, 26 : 782 - 801
[44] Estimation of two high-dimensional covariance matrices and the spectrum of their ratio
Wen, Jun
[J]. JOURNAL OF MULTIVARIATE ANALYSIS, 2018, 168 : 1 - 29
[45] Test on the linear combinations of covariance matrices in high-dimensional data
Zhidong Bai
Jiang Hu
Chen Wang
Chao Zhang
[J]. Statistical Papers, 2021, 62 : 701 - 719
[46] Homogeneity tests of covariance matrices with high-dimensional longitudinal data
Zhong, Ping-Shou
Li, Runze
Santo, Shawn
[J]. BIOMETRIKA, 2019, 106 (03) : 619 - 634
[47] Parallel computation of high-dimensional robust correlation and covariance matrices
Chilson, James
Ng, Raymond
Wagner, Alan
Zamar, Ruben
[J]. ALGORITHMICA, 2006, 45 (03) : 403 - 431
[48] Homogeneity test of several covariance matrices with high-dimensional data
Qayed, Abdullah
Han, Dong
[J]. JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 2021, 31 (04) : 523 - 540
[49] Multi-sample test for high-dimensional covariance matrices
Zhang, Chao
Bai, Zhidong
Hu, Jiang
Wang, Chen
[J]. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2018, 47 (13) : 3161 - 3177
[50] HIGH-DIMENSIONAL SPARSE BAYESIAN LEARNING WITHOUT COVARIANCE MATRICES
Lin, Alexander
Song, Andrew H.
Bilgic, Berkin
Ba, Demba
[J]. 2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2022, : 1511 - 1515

← 1 2 3 4 5 →