Testing for independence in high dimensions based on empirical copulas
Author:
Affiliation:
1. Fakultät für Mathematik, Ruhr-Universität Bochum
2. Université Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK
Publisher
Institute of Mathematical Statistics
Reference32 articles.
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3. CORMEN, T. H., LEISERSON, C. E., RIVEST, R. L. and STEIN, C. (2009). Introduction to Algorithms, 3rd ed. MIT Press, Cambridge, MA.
4. LEDOIT, O. and WOLF, M. (2002). Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size. Ann. Statist. 30 1081–1102.
5. Yao, S., Zhang, X. and Shao, X. (2018). Testing mutual independence in high dimension via distance covariance. J. R. Stat. Soc. Ser. B. Stat. Methodol. 80 455–480.
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