The purpose of this paper is to provide a robust statistical procedure for evaluating and measuring the relative efficiency of multiple decision‐making units. This robust approach is based on the generalized maximum entropy principle.
Information‐theoretic estimation approach is employed in this paper and a comparison is made with the classical relative efficiency (CCR) by using a non‐parametric bootstrap simulation. A real data application on the research performance of faculty members at Yarmouk University is presented.
Results indicate that the relative efficiency based on the generalized maximum entropy estimation approach is more accurate, costs less and is more efficient than the CCR relative efficiency.
Owing to use of Shannon's entropy formulation, it is still critical whether the results also hold with cross entropy or a higher order entropy formulation for modeling additive, multiplicative or partial relative efficiency.
A super data envelopment analysis has been introduced for finding superior decision‐making units (DMU) by solving only one nonlinear programming system, which could be considered as a flexible tool for modeling multiple input‐output DMU.
