Rank-based indices for testing independence between two high-dimensional vectors-Reference-Cited by-同舟云学术

Rank-based indices for testing independence between two high-dimensional vectors

Published:2024-02-01 Issue:1 Volume:52 Page:
ISSN:0090-5364
Container-title:The Annals of Statistics
language:
Short-container-title:Ann. Statist.

Author:

Zhou Yeqing¹,Xu Kai²,Zhu Liping³,Li Runze⁴

Affiliation:

1. School of Mathematical Sciences and Key Laboratory of Intelligent Computing and Applications, Tongji University

2. School of Mathematics and Statistics, Anhui Normal University

3. Center for Applied Statistics, Institute of Statistics and Big Data, Renmin University of China Zhijiang Institute of Big Data and Statistics, School Statistics and Mathematics, Zhejiang Gongshang University

4. Department of Statistics, The Pennsylvania State University at University Park

Publisher

Institute of Mathematical Statistics

Reference49 articles.

1. Kendall, M. G. (1938). A new measure of rank correlation. Biometrika 30 81–93.

2. Heller, R., Heller, Y. and Gorfine, M. (2013). A consistent multivariate test of association based on ranks of distances. Biometrika 100 503–510.

3. ALBERT, M., LAURENT, B., MARREL, A. and MEYNAOUI, A. (2022). Adaptive test of independence based on HSIC measures. Ann. Statist. 50 858–879.

4. Bergsma, W. and Dassios, A. (2014). A consistent test of independence based on a sign covariance related to Kendall’s tau. Bernoulli 20 1006–1028.

5. Blum, J. R., Kiefer, J. and Rosenblatt, M. (1961). Distribution free tests of independence based on the sample distribution function. Ann. Math. Stat. 32 485–498.

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