Shape mixtures of multivariate skew-normal distributions

On the multivariate Skew-Normal distribution and its scale mixtures

In this paper we study the multivariate skew-normal distribution and its scale mixtures, as extensions of the similar non-skewed distributions. Different parameterizations and some properties are investigated. ∗ Subject Classification: 60E05.

Rejoinder to the discussion of "Model-based clustering and classification with non-normal mixture distributions"

Non-normal mixture distributions have received increasing attention in recent years. Finite mixtures of multivariate skew-symmetric distributions, in particular, the skew normal and skew t-mixture models, are emerging as promising extensions to the traditional normal and t-mixture models. Most of these parametric families of skew distributions are closely related, and can be classified into fou...

Bayesian Inference for Shape Mixtures of Skewed Distributions, with Application to Regression Analysis

We introduce a class of shape mixtures of skewed distributions and study some of its main properties. We discuss a Bayesian interpretation and some invariance results of the proposed class. We develop a Bayesian analysis of the skew-normal, skew-generalized-normal, skew-normal-t and skew-t-normal linear regression models under some special prior specifications for the model parameters. In parti...

Characteristic functions of scale mixtures of multivariate skew-normal distributions

Modelling asset return using multivariate asymmetric mixture models with applications to estimation of Value-at-Risk

Value-at-Risk (VaR) is a widely used statistical measure in financial risk management for quantifying the level of risk associated with a specific investment portfolio. It is well-known that historical return data exhibit non-normal features, such as heavy tails and skewness. Current analytical (parameteric) calculation of VaR typically assumes the distribution of the portfolio return to be a n...