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

Zhonglu Huang and Gengsheng Qin ()
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Zhonglu Huang: Georgia State University
Gengsheng Qin: Georgia State University

Computational Statistics, 2023, vol. 38, issue 2, No 20, 1055 pages

Abstract: 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.

Keywords: Empirical likelihood; Influence function; Kendall correlation coefficient (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00180-022-01267-8

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