167
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A robust permutation test for Kendall's tau

, , &
Pages 2780-2800 | Received 20 Oct 2022, Accepted 18 Apr 2023, Published online: 03 May 2023

References

  • Joe H. Multivariate models and dependence concepts. London: Chapman & Hall; 1997.
  • Drouet-Mari D, Kotz S. Correlation and dependence. London: Imperial College Press; 2001.
  • Chatterjee S. A new coefficient of correlation. J Am Stat Assoc. 2021;116(536):2009–2022.
  • Tenzer Y, Mandel M, Zuk O. Testing independence under biased sampling. J Am Stat Assoc. 2022;117(540):2194–2206.
  • Susam S, Hudaverdi Ucer B. A goodness-of-fit test based on bézier curve estimation of kendall distribution. J Stat Comput Simul. 2020;90(7):1194–1215.
  • Xu K, Zhu L. Power analysis of projection-pursuit independence tests. Stat Sin. 2022;32:417–433.
  • Zhu L, Xu K, Li R, et al. Projection correlation between two random vectors. Biometrika. 2017;104(4):829–843.
  • Zhang S, Ye K, Wang M. A simple consistent bayes factor for testing the kendall rank correlation coefficient. J Stat Comput Simul. 2023;93(6):888–903.
  • Pearson K. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philos Mag Lett. 1900;50(302):157–175.
  • Kendall M. A new measure of rank correlation. Biometrika. 1938;30(1-2):81–93.
  • Bergsma W, Dassios A. A consistent test of independence based on a sign covariance related to kendall's tau. Bernoulli. 2014;20(2):1006–1028.
  • Huber P, Ronchetti E. Robust statistics. 2nd ed. Hoboken: Wiley; 2009.
  • Lehmann E, Romano J. Testing statistical hypotheses. 3rd ed. New York: Springer; 2005.
  • DiCiccio C, Romano J. Robust permutation tests for correlation and regression coefficients. J Am Stat Assoc. 2017;112(519):1211–1220.
  • Kendall M. Rank and product-moment correlation. Biometrika. 1949;36(1–2):177–193.
  • Chung E, Romano J. Exact and asymptotically robust permutation tests. Ann Stat. 2013;41(2):484–507.
  • Chung E, Romano J. Multivariate and multiple permutation tests. J Econom. 2016;193(1):76–91.
  • Bertanha M, Chung E. Permutation tests at nonparametric rates. J Am Stat Assoc. 2022;1–14. doi:10.1080/01621459.2022.2087660
  • Mukherjee A, Murakami H. Multivariate kruskalcwallis tests based on principal component score and latent source of independent component analysis. Aust N Z J Stat. 2022;64(3):356–380.
  • Spearman C. The proof and measurement of association between two things. Am J Psychol. 1904;15(1):72–101.
  • Bonnini S, Corain L, Marozzi M, et al. Nonparametric hypothesis testing: rank and permutation methods with applications in R. John Wiley & Sons; 2014.
  • Nelsen R. An introduction to copulas. New York: Springer; 2006.
  • Sklar A. Fonctions de répartition à n dimensions et leurs marges. Publ. Inst. Stat. Univ. Paris. 1959;8:229–231.
  • Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. 2nd ed. New York: Springer; 2009.
  • Shapiro S, Wilk M. An analysis of variance test for normality. Biometrika. 1965;52(3-4):591–611.
  • Székely G, Rizzo M. A new test for multivariate normality. J Multivar Anal. 2005;93(1):58–80.
  • Han F, Chen S, Liu H. Distribution-free tests of independence in high dimensions. Biometrika. 2017;104(4):813–828.
  • Leung D, Drton M. Testing independence in high dimensions with sums of rank correlations. Ann Stat. 2018;46(1):280–307.
  • Leucht A, Neumann M. Consistency of general bootstrap methods for degenerate u-type and v-type statistics. J Multivar Anal. 2009;100(8):1622–1633.
  • Hoeffding W. A class of statistics with asymptotically normal distribution. Ann Math Stat. 1948;19(3):293–325.
  • Serfling R. Approximation theorems in mathematical statistics. Wiley: New York; 1980.
  • Massart P. The tight constant in the dvoretzky-kiefer-wolfowitz inequality. Ann Probab. 1990;18(3):1269–1283.
  • Wolfowitz J. Generalization of the theorem of glivenko-cantelli. Ann Math Stat. 1954;25(1):131–138.
  • van der Vaart A. Asymptotic statistics. New York: Cambridge University Press; 2000.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.