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DEA by sequential exclusion of alternatives in heterogeneous samples

Fuad Aleskerov () and Vsevolod Petrushchenko ()

International Journal of Information Technology & Decision Making (IJITDM), 2016, vol. 15, issue 01, 5-22

Abstract: Data Envelopment Analysis (DEA) is a well-known nonparametric technique of efficiency evaluation which is actively used in many economic applications. However, DEA is not very well applicable when a sample consists of firms operating under drastically different conditions. We offer a new method of efficiency estimation in heterogeneous samples based on a sequential exclusion of alternatives and standard DEA approach. We show a connection between efficiency scores obtained via standard DEA model and the ones obtained via our algorithm. We also illustrate our model by evaluating 28 Russian universities and compare the results obtained by two techniques.

Keywords: Data envelopment analysis (DEA); efficiency; sequential exclusion of alternatives; heterogeneity; universities’ efficiency (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1142/S021962201550042X

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