A review of 20 years of naive tests of significance for high-dimensional mean vectors and covariance matrices

被引：0

作者：

HU Jiang ^{[1
]}

BAI ZhiDong ^{[1
]}

机构：

[1] Key Laboratory for Applied Statistics of Ministry of Education,Northeast Normal University

来源：

Science China Mathematics | 2016年 / 59卷 / 12期

基金：

中国国家自然科学基金;

关键词：

naive testing methods; hypothesis testing; high-dimensional data; multivariate analysis of variance(MANOVA);

D O I：

暂无

中图分类号：

O212.1 [一般数理统计];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We introduce the so-called naive tests and give a brief review of the new developments. Naive testing methods are easy to understand and perform robustly, especially when the dimension is large. We focus mainly on reviewing some naive testing methods for the mean vectors and covariance matrices of high-dimensional populations, and we believe that this naive testing approach can be used widely in many other testing problems.

引用

页码：2281 / 2300

页数：20

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