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Influence function-based confidence intervals for the Kendall rank correlation coefficient

Author

Listed:
  • Zhonglu Huang

    (Georgia State University)

  • Gengsheng Qin

    (Georgia State University)

Abstract

Correlation coefficients measure the association between two random variables. In circumstances in which the typically-used Pearson correlation coefficient does not suffice, the Kendall rank correlation coefficient is routinely used as an alternative measure. In this paper, using the influence function of the Kendall rank correlation coefficient, we develop a normal approximation-based confidence interval and an empirical likelihood-based confidence interval for the Kendall rank correlation coefficient. Simulation studies are conducted to show their good finite sample properties and robustness. We apply the proposed methods to a real dataset on Bitcoin financial data.

Suggested Citation

  • Zhonglu Huang & Gengsheng Qin, 2023. "Influence function-based confidence intervals for the Kendall rank correlation coefficient," Computational Statistics, Springer, vol. 38(2), pages 1041-1055, June.
    • Handle: RePEc:spr:compst:v:38:y:2023:i:2:d:10.1007_s00180-022-01267-8
    DOI: 10.1007/s00180-022-01267-8
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    References listed on IDEAS

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    1. Christophe Croux & Catherine Dehon, 2010. "Influence functions of the Spearman and Kendall correlation measures," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(4), pages 497-515, November.
    2. Xinjie Hu & Aekyung Jung & Gengsheng Qin, 2020. "Interval Estimation for the Correlation Coefficient," The American Statistician, Taylor & Francis Journals, vol. 74(1), pages 29-36, January.
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