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A new test of independence for high-dimensional data

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  • Mao, Guangyu

Abstract

This paper proposes a new statistic to test independence of high-dimensional data. The simulation results suggest that the performance of the test based on our statistic is comparable to the existing ones, and under some circumstances it may have higher power. Therefore, the new statistic can be employed in practice as an alternative choice.

Suggested Citation

  • Mao, Guangyu, 2014. "A new test of independence for high-dimensional data," Statistics & Probability Letters, Elsevier, vol. 93(C), pages 14-18.
    • Handle: RePEc:eee:stapro:v:93:y:2014:i:c:p:14-18
    DOI: 10.1016/j.spl.2014.05.024
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    References listed on IDEAS

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    1. James R. Schott, 2005. "Testing for complete independence in high dimensions," Biometrika, Biometrika Trust, vol. 92(4), pages 951-956, December.
    2. Ibrahim J. G. & Chen M-H. & Gray R. J., 2002. "Bayesian Models for Gene Expression With DNA Microarray Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 88-99, March.
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    Cited by:

    1. 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).
    2. Mao, Guangyu, 2018. "Testing independence in high dimensions using Kendall’s tau," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 128-137.
    3. Mao, Guangyu, 2015. "A note on testing complete independence for high dimensional data," Statistics & Probability Letters, Elsevier, vol. 106(C), pages 82-85.

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    Keywords

    High dimension; Independence test;

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