A Regression Perspective on Generalized Distance Covariance and the Hilbert–Schmidt Independence Criterion-Reference-Cited by-同舟云学术

A Regression Perspective on Generalized Distance Covariance and the Hilbert–Schmidt Independence Criterion

Published:2022-11-01 Issue:4 Volume:37 Page:
ISSN:0883-4237
Container-title:Statistical Science
language:
Short-container-title:Statist. Sci.

Author:

Edelmann Dominic¹,Goeman Jelle²

Affiliation:

1. Dominic Edelmann is Professor, German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany

2. Jelle Goeman is Postdoctoral Researcher, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, Netherlands

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,General Mathematics,Statistics and Probability

Reference71 articles.

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2. Székely, G. J. and Rizzo, M. L. (2013). Energy statistics: A class of statistics based on distances. J. Statist. Plann. Inference 143 1249–1272.

3. BERG, C., CHRISTENSEN, J. P. R. and RESSEL, P. (1984). Harmonic Analysis on Semigroups: Theory of Positive Definite and Related Functions. Graduate Texts in Mathematics 100. Springer, New York.

4. BERSCHNEIDER, G. and BÖTTCHER, B. (2019). On complex Gaussian random fields, Gaussian quadratic forms and sample distance multivariance. Preprint. arXiv:1808.07280.

5. BÖTTCHER, B., KELLER-RESSEL, M. and SCHILLING, R. L. (2018). Detecting independence of random vectors: Generalized distance covariance and Gaussian covariance. Modern Stoch. Theory Appl. 5 353–383.

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