Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/82063
Type: Artigo
Title: Multivariate skew-normal generalized hyperbolic distribution and its properties
Author: Vilca, Filidor
Balakrishnan, N.
Zeller, Camila Borelli
Abstract: The Generalized Inverse Gaussian (GIG) distribution has found many interesting applications: see Jorgensen [24]. This rich family includes some well-known distributions, such as the inverse Gaussian, gamma and exponential, as special cases. These distributions have been used as the mixing density for building some heavy-tailed multivariate distributions including the normal inverse Gaussian, Student-t and Laplace distributions. In this paper, we use the GIG distribution in the context of the scale-mixture of skew-normal distributions, deriving a new family of distributions called Skew-Normal Generalized Hyperbolic distributions. This new flexible family of distributions possesses skewness with heavy-tails, and generalizes the symmetric normal inverse Gaussian and symmetric generalized hyperbolic distributions. (C) 2014 Elsevier Inc. All rights reserved.
The Generalized Inverse Gaussian (GIG) distribution has found many interesting applications: see Jorgensen [24]. This rich family includes some well-known distributions, such as the inverse Gaussian, gamma and exponential, as special cases. These distributions have been used as the mixing density for building some heavy-tailed multivariate distributions including the normal inverse Gaussian, Student-t and Laplace distributions. In this paper, we use the GIG distribution in the context of the scale-mixture of skew-normal distributions, deriving a new family of distributions called Skew-Normal Generalized Hyperbolic distributions. This new flexible family of distributions possesses skewness with heavy-tails, and generalizes the symmetric normal inverse Gaussian and symmetric generalized hyperbolic distributions.
Subject: Estatística descritiva
Distribuição (Probabilidades)
Distribuição normal assimétrica
Distribuição gaussiana inversa
Distribuições bivariadas (Estatística)
Processos gaussianos
Modelos mistos
Country: Estados Unidos
Editor: Elsevier
Citation: Journal Of Multivariate Analysis. Elsevier Inc, v. 128, n. 73, n. 85, 2014.
Rights: fechado
Identifier DOI: 10.1016/j.jmva.2014.03.002
Address: https://www.sciencedirect.com/science/article/pii/S0047259X14000499
Date Issue: 2014
Appears in Collections:IMECC - Artigos e Outros Documentos

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