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On the generalized multivariate Gumbel distribution

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  • Demirhan, Haydar
  • Kalaylioglu, Zeynep

Abstract

In this article, main characteristics, marginal, joint, and conditional inferences of a generalized multivariate Gumbel model are derived, and random vector generation is described. Distribution of the sum where summands come from a bivariate generalized multivariate Gumbel distribution is derived.

Suggested Citation

  • Demirhan, Haydar & Kalaylioglu, Zeynep, 2015. "On the generalized multivariate Gumbel distribution," Statistics & Probability Letters, Elsevier, vol. 103(C), pages 93-99.
    • Handle: RePEc:eee:stapro:v:103:y:2015:i:c:p:93-99
    DOI: 10.1016/j.spl.2015.04.023
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    References listed on IDEAS

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    1. Yang, Yang & Leipus, Remigijus & Šiaulys, Jonas, 2014. "Closure property and maximum of randomly weighted sums with heavy-tailed increments," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 162-170.
    2. Joe, Harry & Seshadri, Vanamamalai & Arnold, Barry C., 2012. "Multivariate inverse Gaussian and skew-normal densities," Statistics & Probability Letters, Elsevier, vol. 82(12), pages 2244-2251.
    3. Adelchi Azzalini, 2005. "The Skew‐normal Distribution and Related Multivariate Families," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 32(2), pages 159-188, June.
    4. Furman, Edward, 2008. "On a multivariate gamma distribution," Statistics & Probability Letters, Elsevier, vol. 78(15), pages 2353-2360, October.
    5. Modarres, Reza, 2011. "High-dimensional generation of Bernoulli random vectors," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1136-1142, August.
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