‎Here we consider  a new class of skew logistic  distribution as a generalized mixture of the standard logistic and skew logistic distributions‎, ‎and study some of its important aspects‎. ‎The tail behaviour of the  distribution is compared with that of the skew logistic distribution and a location-scale extension of the distribution is proposed‎. ‎Fu...

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...

A Bayesian analysis of a skewed exponential power distribution has been performed. The skewed exponential power family includes the symmetric exponential power distribution as a particular case and provides flexible distributions with lighter and heavier tails compared to the normal one. The distributions of this family can successfully handle both symmetry/asymmetry and light/heavy tails simul...

We present general results on the identifiability of finite mixtures of elliptical distributions under conditions on the characteristic generators or density generators. Examples include the multivariate t distribution, symmetric stable laws, exponential power and Kotz distributions. In each case, the shape parameter is allowed to vary in the mixture, in addition to the location vector and the ...

This paper proposes a two-stage maximum entropy prior to elicit uncertainty regarding a multivariate interval constraint of the location parameter of a scale mixture of normal model. Using Shannon’s entropy, this study demonstrates how the prior, obtained by using two stages of a prior hierarchy, appropriately accounts for the information regarding the stochastic constraint and suggests an obje...

Mixture models form the essential basis of data clustering within a statistical framework. Here, the estimation of the parameters of a mixture of Gaussian densities is considered. In this particular context, it is well known that the maximum likelihood approach is statistically ill posed, i.e. the likelihood function is not bounded above, because of singularities at the boundary of the paramete...