DEA problems under geometrical or probability uncertainties of sample data

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Reihe Ökonomie / Economics Series ; No. 89

Classification and Topics

• Classification: Wirtschaft ; Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access ; Criteria for Decision-Making under Risk and Uncertainty ; State and Local Budget and Expenditures
• Subject: DEA ; sample data uncertainty ; linear programming ; minimax optimization ; stochastic optimization ; stochastic frontier functions ; DEA ; Ungewissheit von Daten ; lineare Programmierung ; minimax-Optimierung ; stochastische Optimierungsmethoden ; stochastische Grenzfunktionen ; Mathematische Optimierung ; Data-Envelopment-Analyse ; Theorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Althaler, Karl S. ; Slavova, Tatjana
• Event: Veröffentlichung
• (who): Institute for Advanced Studies (IHS)
• (where): Vienna
• (when): 2000

Further information

• Handle: http://hdl.handle.net/10419/71249
• Last update: 10.03.2025, 11:45 AM CET