DEA Problems under Geometrical or Probability Uncertainties of Sample Data

Karl S. Althaler and Tatjana Slavova
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Karl S. Althaler: Institute for Advanced Studies, Vienna
Tatjana Slavova: Institute for Advanced Studies, Vienna

No 89, Economics Series from Institute for Advanced Studies

Abstract: This paper discusses the theoretical and practical aspects of new methods for solving DEA problems under real-life geometrical uncertainty and probability uncertainty of sample data. The proposed minimax approach to solve problems with geometrical uncertainty of sample data involves an implementation of linear programming or minimax optimization, whereas the problems with probability uncertainty of sample data are solved through implementing of econometric and new stochastic optimization methods, using the stochastic frontier functions estimation.

Keywords: DEA; Sample data uncertainty; Linear programming; Minimax optimization; Stochastic optimization; Stochastic frontier functions (search for similar items in EconPapers)
JEL-codes: C81 D81 H72 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2000-10
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Citations: View citations in EconPapers (1)

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https://irihs.ihs.ac.at/id/eprint/1295 First version, 2000 (application/pdf)

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