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Kendall distribution functions

Author

Listed:
  • Nelsen, Roger B.
  • Quesada-Molina, José Juan
  • Rodríguez-Lallena, José Antonio
  • Úbeda-Flores, Manuel

Abstract

If X and Y are continuous random variables with joint distribution function H, then the Kendall distribution function of (X,Y) is the distribution function of the random variable H(X,Y). Kendall distribution functions arise in the study of stochastic orderings of random vectors. In this paper we study various properties of Kendall distribution functions for both populations and samples.

Suggested Citation

  • Nelsen, Roger B. & Quesada-Molina, José Juan & Rodríguez-Lallena, José Antonio & Úbeda-Flores, Manuel, 2003. "Kendall distribution functions," Statistics & Probability Letters, Elsevier, vol. 65(3), pages 263-268, November.
    • Handle: RePEc:eee:stapro:v:65:y:2003:i:3:p:263-268
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    References listed on IDEAS

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    1. Genest, Christian & Rivest, Louis-Paul, 2001. "On the multivariate probability integral transformation," Statistics & Probability Letters, Elsevier, vol. 53(4), pages 391-399, July.
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    Cited by:

    1. Segers, Johan & Uyttendaele, Nathan, 2014. "Nonparametric estimation of the tree structure of a nested Archimedean copula," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 190-204.
    2. Abdulhamid A. Alzaid & Weaam M. Alhadlaq, 2023. "A New Family of Archimedean Copulas: The Half-Logistic Family of Copulas," Mathematics, MDPI, vol. 12(1), pages 1-18, December.
    3. Fuchs, Sebastian & Schmidt, Klaus D., 2021. "On order statistics and Kendall’s tau," Statistics & Probability Letters, Elsevier, vol. 169(C).
    4. Nowak, Claus P. & Konietschke, Frank, 2021. "Simultaneous inference for Kendall’s tau," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    5. Christian Genest & Johanna Nešlehová & Johanna Ziegel, 2011. "Inference in multivariate Archimedean copula models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 223-256, August.
    6. Nappo Giovanna & Spizzichino Fabio, 2020. "Relations between ageing and dependence for exchangeable lifetimes with an extension for the IFRA/DFRA property," Dependence Modeling, De Gruyter, vol. 8(1), pages 1-33, January.
    7. Fountain, Robert L. & Herman Jr., John R. & Rustvold, D. Leif, 2008. "An application of Kendall distributions and alternative dependence measures: SPX vs. VIX," Insurance: Mathematics and Economics, Elsevier, vol. 42(2), pages 469-472, April.
    8. Sabrina Mulinacci, 2022. "A Marshall-Olkin Type Multivariate Model with Underlying Dependent Shocks," Methodology and Computing in Applied Probability, Springer, vol. 24(4), pages 2455-2484, December.
    9. Fontanari Andrea & Cirillo Pasquale & Oosterlee Cornelis W., 2020. "Lorenz-generated bivariate Archimedean copulas," Dependence Modeling, De Gruyter, vol. 8(1), pages 186-209, January.
    10. Yeting Du & Johanna Nešlehová, 2013. "A moment-based test for extreme-value dependence," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(5), pages 673-695, July.
    11. Hideatsu Tsukahara, 2011. "Comments on: Inference in multivariate Archimedean copula models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 287-289, August.
    12. Nappo Giovanna & Spizzichino Fabio, 2020. "Relations between ageing and dependence for exchangeable lifetimes with an extension for the IFRA/DFRA property," Dependence Modeling, De Gruyter, vol. 8(1), pages 1-33, January.
    13. Segers, Johan & Uyttendaele, Nathan, 2013. "Nonparametric estimation of the tree structure of a nested Archimedean copula," LIDAM Discussion Papers ISBA 2013009, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    14. Sordo, Miguel A., 2016. "A multivariate extension of the increasing convex order to compare risks," Insurance: Mathematics and Economics, Elsevier, vol. 68(C), pages 224-230.
    15. Fontanari Andrea & Cirillo Pasquale & Oosterlee Cornelis W., 2020. "Lorenz-generated bivariate Archimedean copulas," Dependence Modeling, De Gruyter, vol. 8(1), pages 186-209, January.
    16. Cousin, Areski & Di Bernardino, Elena, 2013. "On multivariate extensions of Value-at-Risk," Journal of Multivariate Analysis, Elsevier, vol. 119(C), pages 32-46.
    17. Elena Di Bernardino & Clémentine Prieur, 2014. "Estimation of multivariate conditional-tail-expectation using Kendall's process," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(2), pages 241-267, June.
    18. Quessy, Jean-François & Durocher, Martin, 2019. "The class of copulas arising from squared distributions: Properties and inference," Econometrics and Statistics, Elsevier, vol. 12(C), pages 148-166.
    19. Erem, Aysegul & Bayramoglu, Ismihan, 2017. "Exact and asymptotic distributions of exceedance statistics for bivariate random sequences," Statistics & Probability Letters, Elsevier, vol. 125(C), pages 181-188.
    20. Holly Brannelly & Andrea Macrina & Gareth W. Peters, 2021. "Stochastic measure distortions induced by quantile processes for risk quantification and valuation," Papers 2201.02045, arXiv.org.

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