Spectral analysis of large dimensional Chatterjee's rank correlation matrix

Fuente: arXiv
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Autori principali: Dong, Zhaorui, Han, Fang, Yao, Jianfeng
Natura: Preprint
Pubblicazione: 2025
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author Dong, Zhaorui
Han, Fang
Yao, Jianfeng
author_facet Dong, Zhaorui
Han, Fang
Yao, Jianfeng
contents This paper studies the spectral behavior of large dimensional Chatterjee's rank correlation matrix when observations are independent draws from a high-dimensional random vector with independent continuous components. We show that the empirical spectral distribution of its symmetrized version converges to the semicircle law, and thus providing the first example of a large correlation matrix deviating from the Marchenko-Pastur law that governs those of Pearson, Kendall, and Spearman. We further establish central limit theorems for linear spectral statistics, which in turn enable the development of Chatterjee's rank correlation-based tests of complete independence among the components.
format Preprint
id arxiv_https___arxiv_org_abs_2510_07262
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Spectral analysis of large dimensional Chatterjee's rank correlation matrix
Dong, Zhaorui
Han, Fang
Yao, Jianfeng
Statistics Theory
Probability
This paper studies the spectral behavior of large dimensional Chatterjee's rank correlation matrix when observations are independent draws from a high-dimensional random vector with independent continuous components. We show that the empirical spectral distribution of its symmetrized version converges to the semicircle law, and thus providing the first example of a large correlation matrix deviating from the Marchenko-Pastur law that governs those of Pearson, Kendall, and Spearman. We further establish central limit theorems for linear spectral statistics, which in turn enable the development of Chatterjee's rank correlation-based tests of complete independence among the components.
title Spectral analysis of large dimensional Chatterjee's rank correlation matrix
topic Statistics Theory
Probability
url https://arxiv.org/abs/2510.07262