The problem of measuring the disparity of a particular probability density function from a normal one has been addressed in several recent studies. The most used technique to deal with the problem has been exact expressions using information measures over particular distributions. In this paper, we consider a class of asymmetric distributions with a normal kernel, called Generalized Skew-Normal...

the  main  assumptions  in  liner  mixed  model  are normality  and  independency  of  random  effect component.   unfortunately,   these   two  assumptions   might   be  unrealistic   in  some   situations. therefore, in this paper, we will discuss about the analysis of bayesian analysis of non-normal and non-independent mixed model using skew-normal/independent distributions, and finally, thi...

For a class of skew-normal matrix distributions, the density function, moment generating function and independent conditions are obtained. The noncentral skew Wishart distribution is defined and the necessary and sufficient conditions under which a quadratic form is noncentral skew Wishart distributed random matrix are established. A new version of Cochran’s theorem is given. For illustration, ...

In this paper we study stochastic dominance rules of first and second order for univariate skew-normal random variables, the analysis being relevant in connection with the problem of portfolio choice in stock markets showing departure from the classical assumption of normality on returns. Besides that, our analysis is also relevant for markets where stocks returns are normally distributed: if s...

In many practical situations, we only know a few first moments of a random variable, and out of all probability distributions which are consistent with this information, we need to select one. When we know the first two moments, we can use the Maximum Entropy approach and get normal distribution. However, when we know the first three moments, the Maximum Entropy approach doe snot work. In such ...

Shannon and Rényi entropies are two important measures of uncertainty for data analysis. These have been studied multivariate Student-t skew-normal distributions. In this paper, we extend the entropy to skew-t finite mixture (FMST) This class flexible distributions allows handling asymmetry tail weight behavior simultaneously. We find upper lower bounds these families. Numerical simulations ill...

We discuss tail behaviors, subexponentiality and extreme value distribution of logarithmic skew-normal random variables. With optimal normalized constants, the asymptotic expansion of the distribution of the normalized maximum of logarithmic skew-normal random variables is derived. It shows that the convergence rate of the distribution of the normalized maximum to the Gumbel extreme value distr...

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...