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Increasing discrimination of DEA evaluation by utilizing distances to anti-efficient frontiers

Shen, Wan-fang, Zhang, Da-qun, Liu, Wenbin, Yang, Guo-liang (2016) Increasing discrimination of DEA evaluation by utilizing distances to anti-efficient frontiers. Computers and Operations Research, 75 . pp. 163-173. ISSN 0305-0548. E-ISSN 1873-765X. (doi:10.1016/j.cor.2016.05.017) (KAR id:60736)

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http://dx.doi.org/10.1016/j.cor.2016.05.017

Abstract

This paper develops three DEA performance indicators for the purpose of performance ranking by using the distances to both the efficient frontier and the anti-efficient frontier to enhance discrimination power of DEA analysis. The standard DEA models and the Inverted DEA models are used to identify the efficient and anti-efficient frontiers respectively. Important issues like possible intersections of the two frontiers are discussed. Empirical studies show that these indicators indeed have much more discrimination power than that of standard DEA models, and produce consistent ranks. Furthermore, three types of simulation experiments under general conditions are carried out in order to test the performance and characterization of the indicators. The simulation results show that the averages of both the Pearson and Spearman correlation coefficients between true efficiency and indicators are higher than those of true efficiency and efficiency scores estimated by the BCC model when sample size is small

Item Type: Article
DOI/Identification number: 10.1016/j.cor.2016.05.017
Uncontrolled keywords: Data envelopment analysis; Discrimination; Efficient frontier; Anti-efficient frontier
Divisions: Faculties > Social Sciences > Kent Business School
Depositing User: Steve Liu
Date Deposited: 06 Mar 2017 12:23 UTC
Last Modified: 06 May 2020 03:15 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/60736 (The current URI for this page, for reference purposes)
Liu, Wenbin: https://orcid.org/0000-0001-5966-6235
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