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Multivariate Extension of Raftery Copula

Author

Listed:
  • Tariq Saali

    (Departement of Mathematics, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

  • Mhamed Mesfioui

    (Département de Mathématiques et D’informatique, Université du Québec à Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada
    Department of Statistics, United Arab Emirates University, Al Ain 15551, United Arab Emirates)

  • Ani Shabri

    (Departement of Mathematics, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

Abstract

This paper introduces a multivariate extension of Raftery copula. The proposed copula is exchangeable and expressed in terms of order statistics. Several properties of this copula are established. In particular, the multivariate Kendall’s tau and Spearman’s rho, as well as the density function, of the suggested copula are derived. The lower and upper tail dependence of the proposed copula are also established. The dependence parameter estimator of this new copula is examined based on the maximum likelihood procedure. A simulation study shows a satisfactory performance of the presented estimator. Finally, the proposed copula is successfully applied to a real data set on black cherry trees.

Suggested Citation

  • Tariq Saali & Mhamed Mesfioui & Ani Shabri, 2023. "Multivariate Extension of Raftery Copula," Mathematics, MDPI, vol. 11(2), pages 1-15, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:2:p:414-:d:1034086
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    References listed on IDEAS

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    4. Genest, Christian & Mesfioui, Mhamed & Schulz, Juliana, 2018. "A new bivariate Poisson common shock model covering all possible degrees of dependence," Statistics & Probability Letters, Elsevier, vol. 140(C), pages 202-209.
    5. Genest, Christian & Rémillard, Bruno & Beaudoin, David, 2009. "Goodness-of-fit tests for copulas: A review and a power study," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 199-213, April.
    6. Joe, Harry & Li, Haijun & Nikoloulopoulos, Aristidis K., 2010. "Tail dependence functions and vine copulas," Journal of Multivariate Analysis, Elsevier, vol. 101(1), pages 252-270, January.
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