High-dimensional Tests for Mean Vector: Approaches without Estimating the Mean Vector Directly  

作　　者：Bo CHEN Hai-meng WANG 

机构地区：[1]School of Mathematics and Information Technology,Jiangsu Second Normal University,Nanjing 211200,China

出　　处：《Acta Mathematicae Applicatae Sinica》2022年第1期78-86,共9页应用数学学报（英文版）

摘　　要：Several tests for multivariate mean vector have been proposed in the recent literature.Generally,these tests are directly concerned with the mean vector of a high-dimensional distribution.The paper presents two new test procedures for testing mean vector in large dimension and small samples.We do not focus on the mean vector directly,which is a different framework from the existing choices.The first test procedure is based on the asymptotic distribution of the test statistic,where the dimension increases with the sample size.The second test procedure is based on the permutation distribution of the test statistic,where the sample size is fixed and the dimension grows to infinity.Simulations are carried out to examine the finite-sample performance of the tests and to compare them with some popular nonparametric tests available in the literature.

关 键 词：asymptotic distribution high-dimensional data permutation test U-STATISTIC testing mean vector 

分 类 号：O212.1[理学—概率论与数理统计]