Open AccessA Permutation Test for High-Dimensional Covariance Matrix
Author(s) -
Shanshan Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1865/4/042027
Subject(s) - dimension (graph theory) , covariance matrix , mathematics , permutation (music) , covariance , sample size determination , resampling , gaussian , sample (material) , estimation of covariance matrices , matrix (chemical analysis) , data matrix , statistical hypothesis testing , test (biology) , statistics , combinatorics , physics , clade , paleontology , biochemistry , materials science , chemistry , quantum mechanics , biology , gene , acoustics , composite material , thermodynamics , phylogenetic tree
In the case of “big p and small n”, classical statistical methods and theories are difficult to apply to high-dimensional data problems. This article considers testing the equality of two sample covariance matrices. When both the dimension p and the sample size n tend to infinity, a permutation method is proposed. The new test method eliminates the limitation of sample distribution and dimension. Numerical research shows that the proposed test method has good results in both normal and non-Gaussian distributions under high-dimensional data.