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Robust test for independence in high dimensions

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  • Guangyu Mao

Abstract

This article develops a new test based on Spearman’s rank correlation coefficients for total independence in high dimensions. The test is robust to the non normality and heavy tails of the data, which is a merit that is not shared by the existing tests in literature. Simulation results suggest that the new test performs well under several typical null and alternative hypotheses. Besides, we employ a real data set to illustrate the use of the new test.

Suggested Citation

  • Guangyu Mao, 2017. "Robust test for independence in high dimensions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(20), pages 10036-10050, October.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:20:p:10036-10050
    DOI: 10.1080/03610926.2016.1228965
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    Cited by:

    1. Wu, Zeyu & Wang, Cheng, 2022. "Limiting spectral distribution of large dimensional Spearman’s rank correlation matrices," Journal of Multivariate Analysis, Elsevier, vol. 191(C).
    2. He, Daojiang & Liu, Huanyu & Xu, Kai & Cao, Mingxiang, 2021. "Generalized Schott type tests for complete independence in high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    3. Mao, Guangyu, 2018. "Testing independence in high dimensions using Kendall’s tau," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 128-137.

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