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Archimedean-based Marshall-Olkin Distributions and Related Copula Functions

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  • Sabrina Mulinacci

Abstract

A new class of bivariate distributions is introduced that extends the Generalized Marshall-Olkin distributions of Li and Pellerey (2011). Their dependence structure is studied through the analysis of the copula functions that they induce. These copulas, that include as special cases the Generalized Marshall-Olkin copulas and the Scale Mixture of Marshall-Olkin copulas (see Li, 2009),are obtained through suitable distortions of bivariate Archimedean copulas: this induces asymmetry, and the corresponding Kendall's tau as well as the tail dependence parameters are studied.

Suggested Citation

  • Sabrina Mulinacci, 2015. "Archimedean-based Marshall-Olkin Distributions and Related Copula Functions," Papers 1502.01912, arXiv.org.
  • Handle: RePEc:arx:papers:1502.01912
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    References listed on IDEAS

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    1. Fabrizio Durante & José Quesada-Molina & Carlo Sempi, 2007. "A Generalization of the Archimedean Class of Bivariate Copulas," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(3), pages 487-498, September.
    2. Capéraà, Philippe & Fougères, Anne-Laure & Genest, Christian, 2000. "Bivariate Distributions with Given Extreme Value Attractor," Journal of Multivariate Analysis, Elsevier, vol. 72(1), pages 30-49, January.
    3. Li, Xiaohu & Pellerey, Franco, 2011. "Generalized Marshall-Olkin distributions and related bivariate aging properties," Journal of Multivariate Analysis, Elsevier, vol. 102(10), pages 1399-1409, November.
    4. German Bernhart & Marcos Escobar Anel & Jan-Frederik Mai & Matthias Scherer, 2013. "Default models based on scale mixtures of Marshall-Olkin copulas: properties and applications," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(2), pages 179-203, February.
    5. Li, Haijun, 2009. "Orthant tail dependence of multivariate extreme value distributions," Journal of Multivariate Analysis, Elsevier, vol. 100(1), pages 243-256, January.
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