A High-Dimensional Test for Multivariate Analysis of Variance Under a Low-Dimensional Factor Structure

作     者：Mingxiang Cao Yanling Zhao Kai Xu Daojiang He Xudong Huang 

作者机构：Department of StatisticsAnhui Normal UniversityWuhu 241000People’s Republic of China 

出 版 物：《Communications in Mathematics and Statistics》 (数学与统计通讯（英文）)

年 卷 期：2022年第10卷第4期

页      面：581-597页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：supported by the National Statistical Science Research Program(No.2020LY002) the National Natural Science Foundation of China(Nos.11601008,11526070) Doctor Startup Foundation of Anhui Normal University(No.2016XJJ101) supported by Anhui Provincial Natural Science Foundation(No.2008085MA08) supported by Anhui Provincial Natural Science Foundation(No.1908085MA20) 

主　　题：High-dimensional data MANOVA Low-dimensional factor structure Chi-square distribution 

摘      要：In this paper,the problem of high-dimensional multivariate analysis of variance is investigated under a low-dimensional factor structure which violates some vital assumptions on covariance matrix in some existing *** propose a new test and derive that the asymptotic distribution of the test statistic is a weighted distribution of chi-squares of 1 degree of freedom under the null hypothesis and mild *** provide numerical studies on both sizes and powers to illustrate performance of the proposed test.