Name
Title | ||
|---|---|---|
Dea Model Considering Outputs With Stochastic Noise And A Heavy-Tailed (Stable) Distribution |
Abstract | ||
|---|---|---|
Data envelopment analysis (DEA) is a nonparametric data-based approach that is used to evaluate the performance of a set of homogeneous units; hence, inputs and outputs play a crucial role in this evaluation. In many technologies, the inputs and outputs are so volatile and complex that even other stochastic events can have an influence in the measurement accuracy. A suitable estimation method can be selected according to the type of stochastic model used. If the model has stochastic noises, then in the first step, the noise must be identified by its stochastic behaviour as well as a probability distribution. In DEA, it is important to transform the probabilistic form of a model to its equivalent deterministic model. The chance-constrained programming input (output) oriented model with a normal distribution assumption is most often applied in stochastic cases. However, this study improves the assumption by using a family of heavy-tailed distributions, which is called Stable Distributions. It is assumed that the inputs are deterministic and that the outputs include stochastic noise with a stable distribution. The interesting properties of the selected distribution family help us to solve the probability constraints. A numerical example is provided to illustratively validate the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1080/03155986.2019.1624476 | INFOR |
Keywords | DocType | Volume |
Chance constrained programming, stable distributions, singular value decomposition, symmetric asymptotically Pareto property, bootstrap | Journal | 58 |
Issue | ISSN | Citations |
1 | 0315-5986 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hassan Naseri | 1 | 0 | 0.34 |
| S. Esmaeil Najafi | 2 | 0 | 0.34 |
| Abbas Saghaei | 3 | 41 | 7.70 |