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 

机构地区：[1]Department of Statistics,Anhui Normal University,Wuhu 241000,People’s Republic of China

出　　处：《Communications in Mathematics and Statistics》2022年第4期581-597,共17页数学与统计通讯（英文）

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).

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 literature.We 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 conditions.We provide numerical studies on both sizes and powers to illustrate performance of the proposed test.

关 键 词：High-dimensional data MANOVA Low-dimensional factor structure Chi-square distribution 

分 类 号：O17[理学—数学]