Journal of Applied Mathematics

A Distribution-Free Approach to Stochastic Efficiency Measurement with Inclusion of Expert Knowledge

Kerry Khoo-Fazari, Zijiang Yang, and Joseph C. Paradi

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This paper proposes a new efficiency benchmarking methodology that is capable of incorporating probability while still preserving the advantages of a distribution-free and nonparametric modeling technique. This new technique developed in this paper will be known as the DEA-Chebyshev model. The foundation of DEA-Chebyshev model is based on the model pioneered by Charnes, Cooper, and Rhodes in 1978 known as Data Envelopment Analysis (DEA). The combination of normal DEA with DEA-Chebyshev frontier (DCF) can successfully provide a good framework for evaluation based on quantitative data and qualitative intellectual management knowledge. The simulated dataset was tested on DEA-Chebyshev model. It has been statistically shown that this model is effective in predicting a new frontier, whereby DEA efficient units can be further differentiated and ranked. It is an improvement over other methods, as it is easily applied, practical, not computationally intensive, and easy to implement.

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J. Appl. Math., Volume 2013 (2013), Article ID 102163, 21 pages.

First available in Project Euclid: 14 March 2014

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Khoo-Fazari, Kerry; Yang, Zijiang; Paradi, Joseph C. A Distribution-Free Approach to Stochastic Efficiency Measurement with Inclusion of Expert Knowledge. J. Appl. Math. 2013 (2013), Article ID 102163, 21 pages. doi:10.1155/2013/102163.

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