Stochastic DEA with Using of Skew-Normal Distribution in Error Structure

Symmetric Error Structure in Stochastic DEA

Probability-possibility DEA model with Fuzzy random data in presence of skew-Normal distribution

Data envelopment analysis (DEA) is a mathematical method to evaluate the performance of decision-making units (DMU). In the performance evaluation of an organization based on the classical theory of DEA, input and output data are assumed to be deterministic, while in the real world, the observed values of the inputs and outputs data are mainly fuzzy and random. A normal distribution is a contin...

symmetric error structure in stochastic dea

Comparing the Efficiency of Dmus with Normal and Skew-Normal Distribution using Data Envelopment Analysis

  Data envelopment analysis (DEA) is a nonparametric approach to evaluate theefficiency of decision making units (DMU) using mathematical programmingtechniques. Almost, all of the previous researches in stochastic DEA have been usedthe stochastic data when the inputs and outputs are normally distributed. But, thisassumption may not be true in practice. Therefore, using a normal distribution wi...

Stochastic volatility model with leverage and asymmetrically heavy-tailed error using GH skew Student's t-distribution

Bayesian analysis of a stochastic volatility model with a generalized hyperbolic (GH) skew Student’s t-error distribution is described where we first consider an asymmetric heavy-tailness as well as leverage effects. An efficient Markov chain Monte Carlo estimation method is described exploiting a normal variance-mean mixture representation of the error distribution with an inverse gamma distri...

A Two-parameter Balakrishnan Skew-normal Distribution

In this paper, we discuss a generalization of Balakrishnan skew-normal distribution with two parameters that contains the skew-normal, the Balakrishnan skew-normal and the two-parameter generalized skew-normal distributions as special cases. Furthermore, we establish some useful properties and two extensions of this distribution.