This paper is concerned with the multivariate extended skew-normal [MESN] and skew-Student [MEST] distributions, that is, distributions in which location parameters of underlying truncated are not zero. The extra parameter leads to greater variability moments critical values, thus providing flexibility for empirical work. It reported this various theoretical properties notably limiting forms as...

The aim of this work is to provide the tools to compute the well-known Kullback–Leibler divergence measure for the flexible family of multivariate skew-normal distributions. In particular, we use the Jeffreys divergence measure to compare the multivariate normal distribution with the skew-multivariate normal distribution, showing that this is equivalent to comparing univariate versions of these...

Current opinion regarding the selection of link function in binary response models is that the probit and logit links give essentially similar results. This seems to be true for univariate binary response models; however, for multivariate binary response models such advice is misleading. We address a gap in the literature by empirically examining the relationship between link function selection...

Classes of shape mixtures of independent and dependent multivariate skew-normal distributions are considered and some of their main properties are studied. If interpreted from a Bayesian point of view, the results obtained in this paper bring tractability to the problem of inference for the shape parameter, that is, the posterior distribution can be written in analytic form. Robust inference fo...

We explore extremal properties of a family of skewed distributions extended from the multivariate normal distribution by introducing a skewing function π . We give sufficient conditions on the skewing function for the pairwise asymptotic independence to hold. We apply our results to a special case of the bivariate skew-normal distribution and finally support our conclusions by a simulation stud...

In this paper, we propose finite mixtures of multivariate skew Laplace distributions to model both skewness and heavy-tailedness in the heterogeneous data sets. The maximum likelihood estimators for the parameters of interest are obtained by using the EM algorithm. We give a small simulation study and a real data example to illustrate the performance of the proposed mixture model.

Multivariate ordinal data arise in many areas of applications. This paper proposes new efficient methodology for Bayesian inference for multivariate probit models using Markov chain Monte Carlo techniques. The key idea for our approach is the novel use of parameter expansion to sample correlation matrices. We also propose methodology for model selection. Our approach is demonstrated through sev...

In this talk, the class of multivariate extended skew normal distributions is introduced. The properties of this class of distributions, such as, the moment generating function, probability density function, conditional probability density functions and independence are discussed. The definition of extended noncentral skew chi-square distribution is given. The necessary and sufficient condition...