On testing the equality of high dimensional mean vectors with unequal covariance matrices

2014/06/25 by Jiang Hu, Zhidong Bai, Hu, Jiang +5
Mathematics · #Advanced Statistical Methods and Models #FOS: Mathematics #Random Matrices and Applications #Statistical Methods and Inference #Statistics Theory (math.ST) #math.ST #stat.TH

paper · pdf · doi:10.48550/arxiv.1406.6569

openalex publication_date 2014/06/25 · arxiv created 2015/04/26 · arxiv updated 2015/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this article, we focus on the problem of testing the equality of several high dimensional mean vectors with unequal covariance matrices. This is one of the most important problem in multivariate statistical analysis and there have been various tests proposed in the literature. Motivated by \citetBaiS96E and \citeChenQ10T, a test statistic is introduced and the asymptomatic distributions under the null hypothesis as well as the alternative hypothesis are given. In addition, it is compared with a test statistic recently proposed by \citeSrivastavaK13Ta. It is shown that our test statistic performs much better especially in the large dimensional case.

Citations

• A test for the mean vector with fewer observations than the dimension under non-normality
• A two sample test in high dimensional data
• A test for the mean vector with fewer observations than the dimension

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