A new maximum-type test for high-dimensional correlation matrices

IF 0.9 4区数学 Q3 STATISTICS & PROBABILITY

Statistics & Probability Letters Pub Date : 2025-02-05 DOI:10.1016/j.spl.2025.110365

Jing Chen , Ming Li , Kaige Zhao , Baisen Liu

引用次数: 0

Abstract

The exploration of the structure of high-dimensional correlation matrices has become an increasingly important topic in various fields. This paper aims to develop a novel method for testing the structure of high-dimensional correlation matrices. A new maximum-type test is proposed and the asymptotic distribution is derived, assuming that both the data dimension and the sample size tend towards infinity proportionally. Simulation studies show that our proposed test performs well for the sparse alternatives, dense alternatives, and a mixture of sparse and dense alternatives. Finally, the proposed method is employed to analyze a gene expression dataset.

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来源期刊

Statistics & Probability Letters 数学-统计学与概率论

CiteScore

1.60

自引率

0.00%

发文量

173

审稿时长

6 months

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