Multivariate matrix Mittag–Leffler distributions

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We extend the construction principle of multivariate phase-type distributions to establish an analytically tractable class of heavy-tailed multivariate random variables whose marginal distributions are of Mittag–Leffler type with arbitrary index of regular variation. The construction can essentially be seen as allowing a scalar parameter to become matrix-valued. The class of distributions is shown to be dense among all multivariate positive random variables and hence provides a versatile candidate for the modelling of heavy-tailed, but tail-independent, risks in various fields of application.

Original languageEnglish
JournalAnnals of the Institute of Statistical Mathematics
Volume73
Issue number2
Pages (from-to)369 - 394
ISSN0020-3157
DOIs
Publication statusPublished - 2021

    Research areas

  • Extremes, Heavy tails, Laplace transforms, Markov process, Matrix distribution, Mittag–Leffler distribution, Multivariate distribution, Phase-type

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