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Consistent complete independence test in high dimensions based on Chatterjee correlation coefficient

Author

Listed:
  • Liqi Xia

    (Beijing University of Technology)

  • Ruiyuan Cao

    (Beijing University of Technology)

  • Jiang Du

    (Beijing University of Technology
    Beijing Institute of Scientific and Engineering Computing)

  • Jun Dai

    (Beijing University of Technology)

Abstract

In this article, we consider the complete independence test of high-dimensional data. Based on Chatterjee coefficient, we pioneer the development of quadratic test and extreme value test which possess good testing performance for oscillatory data, and establish the corresponding large sample properties under both null hypotheses and alternative hypotheses. In order to overcome the shortcomings of quadratic statistic and extreme value statistic, we propose a testing method termed as power enhancement test by adding a screening statistic to the quadratic statistic. The proposed method do not reduce the testing power under dense alternative hypotheses, but can enhance the power significantly under sparse alternative hypotheses. Three synthetic data examples and two real data examples are further used to illustrate the performance of our proposed methods.

Suggested Citation

  • Liqi Xia & Ruiyuan Cao & Jiang Du & Jun Dai, 2025. "Consistent complete independence test in high dimensions based on Chatterjee correlation coefficient," Statistical Papers, Springer, vol. 66(1), pages 1-32, January.
    • Handle: RePEc:spr:stpapr:v:66:y:2025:i:1:d:10.1007_s00362-024-01618-1
    DOI: 10.1007/s00362-024-01618-1
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    References listed on IDEAS

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