Large Dimensional Spearman's Rank Correlation Matrices: The Central Limit Theorem and Its Applications

This paper is concerned with Spearman's correlation matrices under large dimensional regime, in which the data dimension diverges to infinity proportionally with the sample size.We establish the central limit theorem for the linear spectral statistics of Spearman's correlation matrices, which extends the results of [Ann.Statist.43(2015Statist.43( ) 2588Statist.43( -2623]].We also study the improved Spearman's correlation matrices [Ann.Math.Statist 19(1948) which is a standard U-statistic of order 3.As applications, we propose three new test statistics for large dimensional independent test and numerical studies demonstrate the applicability of our proposed methods.

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Publication Details

Journal
Statistica Sinica
Published
2025-06-25
DOI
https://doi.org/10.5705/ss.202024.0395
Primary Topic
Advanced Mathematical Theories and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Large Dimensional Spearman's Rank Correlation Matrices: The Central Limit Theorem and Its Applications

Hantao Chen, Cheng Wang
Statistica Sinica
Advanced Mathematical Theories and Applications
article

Large Dimensional Spearman's Rank Correlation Matrices: The Central Limit Theorem and Its Applications

Hantao Chen, Cheng Wang
article en

Abstract

This paper is concerned with Spearman's correlation matrices under large dimensional regime, in which the data dimension diverges to infinity proportionally with the sample size.We establish the central limit theorem for the linear spectral statistics of Spearman's correlation matrices, which extends the results of [Ann.Statist.43(2015Statist.43( ) 2588Statist.43( -2623]].We also study the improved Spearman's correlation matrices [Ann.Math.Statist 19(1948) which is a standard U-statistic of order 3.As applications, we propose three new test statistics for large dimensional independent test and numerical studies demonstrate the applicability of our proposed methods.

Statistica Sinica
Shanghai Jiao Tong University (CN)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 88%
Advanced Mathematical Theories and Applications
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