Skew-symmetric families of distributions are perturbed distributions about a central symmetric distribution, the kernel. Another distribution, the skewing distribution, is used to generate perturbations of the kernel which produces a family of distributions with the kernel as a central member of the family. In this note, it is argued that the choice of skewing distribution does not have a major...

The stochastic data envelopment analysis (SDEA) was developed considering the value ofinputs and outputs as random variables. Therefore, statistical distributions play an importantrole in this regard. The skew-normal (SN) distribution is a family of probability densityfunctions that is frequently used in practical situations. In this paper, we assume that the inputand output variables are skew-...

In this paper, by conditioning on the matrix variate normal distribution (MVND) the construction of the matrix t-type family is considered, thus providing a new perspective of this family. Some important statistical characteristics are given. The presented t-type family is an extension to the work of Dickey [8]. A Bayes estimator for the column covariance matrix &Sigma of MVND is derived under ...

In this paper, we introducte a family of univariate Birnbaum-Saunders distributions arising from the skew-normal-t  distribution. We obtain several properties of this distribution such as its moments, the maximum likelihood estimation procedure via an EM-algorithm and a method to evaluate standard errors using the EM-algorithm. Finally, we apply these methods to a real data set to demonstr...

this paper measures event risk which is introducing by the ball committee to measure the effect of sudden news. this study calculates event risk by using the data of registered firms in tehran stock market between 1993 and 1994. for computing event risk, we use daily market capitalization for 80 firms during 1993-2009, then dividing these firms to three baskets; big, medium and small. the model...

Recently, it has been observed that a new method for generating continuous distributions, T - X family, can be quite effectively used to analyze the data in one dimension. The aim of this study is to generalize this method to two dimensional space so that the marginals would have T - X distributions. So, several examples and properties of this family have been presented. As ...

In a regular distribution family, Cramér-Rao inequality holds, and the maximum likelihood estimator (MLE) converges to a normal distribution whose variance is the inverse of the Fisher information because the Fisher information converges and is well-defined in this family. However, in a non-regular location shift family which is generated by a distribution of R whose support is not R (e.g., a W...