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

Author

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  • Chen, Jing
  • Li, Ming
  • Zhao, Kaige
  • Liu, Baisen

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.

Suggested Citation

  • Chen, Jing & Li, Ming & Zhao, Kaige & Liu, Baisen, 2025. "A new maximum-type test for high-dimensional correlation matrices," Statistics & Probability Letters, Elsevier, vol. 220(C).
    • Handle: RePEc:eee:stapro:v:220:y:2025:i:c:s0167715225000112
    DOI: 10.1016/j.spl.2025.110365
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    References listed on IDEAS

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