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

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 d...

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Bibliographic Details
Published in:Acta Mathematicae Applicatae Sinica 2022, Vol.38 (1), p.78-86
Main Authors: Chen, Bo, Wang, Hai-meng
Format: Article
Language:English
Subjects:
Applications of Mathematics
Asymptotic methods
Math Applications in Computer Science
Mathematical and Computational Physics
Mathematics
Mathematics and Statistics
Permutations
Statistical methods
Test procedures
Theoretical
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Summary: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.
ISSN:0168-9673
1618-3932
DOI:10.1007/s10255-022-1070-z