Distance correlation test for high-dimensional independence-Reference-Cited by-同舟云学术

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Distance correlation test for high-dimensional independence

Published:2024-11-01 Issue:4 Volume:30 Page:
ISSN:1350-7265
Container-title:Bernoulli
language:
Short-container-title:Bernoulli

Author:

Li Weiming¹,Wang Qinwen²,Yao Jianfeng³

Affiliation:

1. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China

2. School of Data Science, Fudan University, Shanghai, China

3. School of Data Science, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China

Publisher

Bernoulli Society for Mathematical Statistics and Probability

Reference31 articles.

1. Chen, S.X., Zhang, L.-X. and Zhong, P.-S. (2010). Tests for high-dimensional covariance matrices. J. Amer. Statist. Assoc. 105 810–819. 10.1198/jasa.2010.tm09560

2. Bai, Z. and Silverstein, J.W. (2010). Spectral Analysis of Large Dimensional Random Matrices, 2nd ed. Springer Series in Statistics. New York: Springer. 10.1007/978-1-4419-0661-8

3. Gao, J., Han, X., Pan, G. and Yang, Y. (2017). High dimensional correlation matrices: The central limit theorem and its applications. J. R. Stat. Soc. Ser. B. Stat. Methodol. 79 677–693. 10.1111/rssb.12189

4. Johnstone, I.M. (2001). On the distribution of the largest eigenvalue in principal components analysis. Ann. Statist. 29 295–327. 10.1214/aos/1009210544

5. Schott, J.R. (2005). Testing for complete independence in high dimensions. Biometrika 92 951–956. 10.1093/biomet/92.4.951

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