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Open AccessArticle

Comparison and Classification of Flexible Distributions for Multivariate Skew and Heavy-Tailed Data

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Department of Applied Mathematics, Computer Science and Statistics, Ghent University, B-9000 Gent, Belgium
2
Vlerick Business School, B-1210 Brussels, Belgium
3
Department of Economics, Ghent University, B-9000 Gent, Belgium
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(10), 1216; https://doi.org/10.3390/sym11101216
Received: 21 July 2019 / Revised: 8 August 2019 / Accepted: 14 September 2019 / Published: 30 September 2019
(This article belongs to the Special Issue Recent Advances on Symmetry in Mathematical Statistics)
We present, compare and classify popular families of flexible multivariate distributions. Our classification is based on the type of symmetry (spherical, elliptical, central symmetry or asymmetry) and the tail behaviour (a single tail weight parameter or multiple tail weight parameters). We compare the families both theoretically (relevant properties and distinctive features) and with a Monte Carlo study (comparing the fitting abilities in finite samples). View Full-Text
Keywords: copulas; elliptical symmetry; multi-tail distributions; skew-elliptical distributions; skewness; transformation approach copulas; elliptical symmetry; multi-tail distributions; skew-elliptical distributions; skewness; transformation approach
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Babić, S.; Ley, C.; Veredas, D. Comparison and Classification of Flexible Distributions for Multivariate Skew and Heavy-Tailed Data. Symmetry 2019, 11, 1216.

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