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Interpoint-ranking sign covariance for the test of independence
[Prediction by supervised principal components]

Author

Listed:
  • Haeun Moon
  • Kehui Chen

Abstract

SummaryWe generalize the sign covariance introduced by Bergsma & Dassios (2014) to multivariate random variables and beyond. The new interpoint-ranking sign covariance is applicable to general types of random objects as long as a meaningful similarity measure can be defined, and it is shown to be zero if and only if the two random variables are independent. The test statistic is a -statistic, whose large-sample behaviour guarantees that the proposed test is consistent against general types of alternatives. Numerical experiments and data analyses demonstrate the superior empirical performance of the proposed method.

Suggested Citation

  • Haeun Moon & Kehui Chen, 2022. "Interpoint-ranking sign covariance for the test of independence [Prediction by supervised principal components]," Biometrika, Biometrika Trust, vol. 109(1), pages 165-179.
    • Handle: RePEc:oup:biomet:v:109:y:2022:i:1:p:165-179.
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    File URL: http://hdl.handle.net/10.1093/biomet/asab011
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    Cited by:

    1. Hongjian Shi & Mathias Drton & Marc Hallin & Fang Han, 2023. "Semiparametrically Efficient Tests of Multivariate Independence Using Center-Outward Quadrant, Spearman, and Kendall Statistics," Working Papers ECARES 2023-03, ULB -- Universite Libre de Bruxelles.

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