A variant of radial measure capable of dealing with negative inputs and outputs in data envelopment analysis
Data envelopment analysis (DEA) is a linear programming methodology to evaluate the relative technical efficiency for each member of a set of peer decision making units (DMUs) with multiple inputs and multiple outputs. It has been widely used to measure performance in many areas. A weakness of the traditional DEA model is that it cannot deal with negative input or output values. There have been many studies exploring this issue, and various approaches have been proposed.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Emrouznejad, Ali & Anouze, Abdel Latef & Thanassoulis, Emmanuel, 2010. "A semi-oriented radial measure for measuring the efficiency of decision making units with negative data, using DEA," European Journal of Operational Research, Elsevier, vol. 200(1), pages 297-304, January.
- Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
- R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
- Lamb, John D. & Tee, Kai-Hong, 2012. "Data envelopment analysis models of investment funds," European Journal of Operational Research, Elsevier, vol. 216(3), pages 687-696.
- Scheel, Holger, 2001. "Undesirable outputs in efficiency valuations," European Journal of Operational Research, Elsevier, vol. 132(2), pages 400-410, July.
- Charnes, A. & Cooper, W. W. & Golany, B. & Seiford, L. & Stutz, J., 1985. "Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 91-107.
- Muliaman D. Hadad & Maximilian J. B. Hall & Wimboh Santoso & Karligash Kenjegalieva & Richard Simper, 2009. "A New Approach to Dealing With Negative Numbers in Efficiency Analysis: An Application to the Indonesian Banking Sector," Discussion Paper Series 2009_20, Department of Economics, Loughborough University, revised Dec 2009.
- Emrouznejad, Ali & Amin, Gholam R. & Thanassoulis, Emmanuel & Anouze, Abdel Latef, 2010. "On the boundedness of the SORM DEA models with negative data," European Journal of Operational Research, Elsevier, vol. 206(1), pages 265-268, October.
- Portela, Maria Conceicao A. Silva & Thanassoulis, Emmanuel, 2007. "Comparative efficiency analysis of Portuguese bank branches," European Journal of Operational Research, Elsevier, vol. 177(2), pages 1275-1288, March.
- Shawna Grosskopf & Kathy J. Hayes & Lori L. Taylor & William L. Weber, 1999. "Anticipating the Consequences of School Reform: A New Use of DEA," Management Science, INFORMS, vol. 45(4), pages 608-620, April.
- Edirisinghe, N.C.P. & Zhang, X., 2007. "Generalized DEA model of fundamental analysis and its application to portfolio optimization," Journal of Banking & Finance, Elsevier, vol. 31(11), pages 3311-3335, November.
- Chambers, Robert G. & Chung, Yangho & Fare, Rolf, 1996. "Benefit and Distance Functions," Journal of Economic Theory, Elsevier, vol. 70(2), pages 407-419, August.
- Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:225:y:2013:i:1:p:100-105. See general information about how to correct material in RePEc.
If references are entirely missing, you can add them using this form.