Cross efficiency evaluation of decision-making units using the maximum decisional efficiency principle

This paper uses maximum decisional efficiency (MDE) principle to derive cross efficiency (CE) scores for input and output oriented frontier efficiency models. The MDE CE models are parametric models that derive their cross efficiencies by either maximizing log-likelihood (input-oriented models) or minimizing negative log-likelihood (output-oriented models). Using real-world and simulated datasets, we compare our MDE models with several competing CE models from the literature. Our results illustrate that the MDE models based CE scores have higher CE averages when inputs are independent or correlated with half-normal inefficiency distributions. We also find that MDE models provide model consensus scores that are highly consistent. When inputs are correlated and inefficiency distributions are exponential, the log-likelihood estimation procedures appear to suffer in performance when compared to data envelopment analysis models with secondary objectives.

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Work Title Cross efficiency evaluation of decision-making units using the maximum decisional efficiency principle
Open Access
  1. Parag C. Pendharkar
  1. Productivity analysis
  2. Maximum likelihood estimation
  3. Frontier models
  4. Data envelopment analysis
License CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)
Work Type Article
  1. Computers and Industrial Engineering
Publication Date May 27, 2020
Publisher Identifier (DOI)
Deposited November 22, 2023




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Work History

Version 1

  • Created
  • Added zCross_Efficiency_MDE-1.docx
  • Added Creator Parag C. Pendharkar
  • Published
  • Updated Keyword, Publication Date Show Changes
    • Productivity analysis, Maximum likelihood estimation, Frontier models, Data envelopment analysis
    Publication Date
    • 2020-07-01
    • 2020-05-27