Multivariate Skew-Normal Generalized Hyperbolic distribution and its properties

被引:32
作者
Vilca, Filidor [1 ]
Balakrishnan, N. [2 ,3 ]
Zeller, Camila Borelli [4 ]
机构
[1] Univ Estadual Campinas, Dept Estat, BR-13081970 Sao Paulo, Brazil
[2] McMaster Univ, Dept Math & Stat, Hamilton, ON, Canada
[3] King Abdulaziz Univ, Dept Stat, Jeddah 21413, Saudi Arabia
[4] Univ Fed Juiz de Fora, Dept Estat, Juiz De Fora, MG, Brazil
基金
巴西圣保罗研究基金会; 加拿大自然科学与工程研究理事会;
关键词
Generalized inverse Gaussian distribution; Skew-normal distribution; Heavy-tailed distributions; Skewness and kurtosis; Normal inverse Gaussian distribution; Skew-Normal Generalized Hyperbolic distribution; Mixtures; SCALE MIXTURES; VARIANCE; MOMENTS; ASSET; MODEL;
D O I
10.1016/j.jmva.2014.03.002
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
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.
引用
收藏
页码:73 / 85
页数:13
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