Limit theorems of Chatterjee's rank correlation

Fuente: arXiv
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Autori principali: Lin, Zhexiao, Han, Fang
Natura: Preprint
Pubblicazione: 2022
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author Lin, Zhexiao
Han, Fang
author_facet Lin, Zhexiao
Han, Fang
contents Establishing the limiting distribution of Chatterjee's rank correlation for a general, possibly non-independent, pair of random variables has been eagerly awaited by many. This paper shows that (a) Chatterjee's rank correlation is asymptotically normal as long as one variable is not a measurable function of the other, (b) the corresponding asymptotic variance is uniformly bounded by 36, and (c) a consistent variance estimator exists. Similar results also hold for Azadkia-Chatterjee's graph-based correlation coefficient, a multivariate analogue of Chatterjee's original proposal. The proof is given by appealing to Hájek representation and Chatterjee's nearest-neighbor CLT.
format Preprint
id arxiv_https___arxiv_org_abs_2204_08031
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Limit theorems of Chatterjee's rank correlation
Lin, Zhexiao
Han, Fang
Statistics Theory
Machine Learning
Probability
Establishing the limiting distribution of Chatterjee's rank correlation for a general, possibly non-independent, pair of random variables has been eagerly awaited by many. This paper shows that (a) Chatterjee's rank correlation is asymptotically normal as long as one variable is not a measurable function of the other, (b) the corresponding asymptotic variance is uniformly bounded by 36, and (c) a consistent variance estimator exists. Similar results also hold for Azadkia-Chatterjee's graph-based correlation coefficient, a multivariate analogue of Chatterjee's original proposal. The proof is given by appealing to Hájek representation and Chatterjee's nearest-neighbor CLT.
title Limit theorems of Chatterjee's rank correlation
topic Statistics Theory
Machine Learning
Probability
url https://arxiv.org/abs/2204.08031