Abstract
Data Envelopment Analysis is not very well applicable when a sample consists of firms operating under drastically different conditions. We offer a new method of efficiency estimation on 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.
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Aleskerov, F., Petrushchenko, V. (2015). DEA for Heterogeneous Samples. In: Le Thi, H., Pham Dinh, T., Nguyen, N. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. Advances in Intelligent Systems and Computing, vol 360. Springer, Cham. https://doi.org/10.1007/978-3-319-18167-7_2
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DOI: https://doi.org/10.1007/978-3-319-18167-7_2
Publisher Name: Springer, Cham
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