Dealing with small samples and dimensionality issues in data envelopment analysis
Data Envelopment Analysis (DEA) is a widely applied nonparametric method for comparative evaluation of firms’ efficiency. A deficiency of DEA is that the efficiency scores assigned to each firm are sensitive to sampling variations, particularly when small samples are used. In addition, an upward bias is present due to dimensionality issues when the sample size is limited compared to the number of inputs and output. As a result, in case of small samples, DEA efficiency scores cannot be considered as reliable measures. The DEA Bootstrap addresses this limitation of the DEA method as it provides the efficiency scores with stochastic properties. However, the DEA Bootstrap is still inappropriate in the presence of small samples. In this context, we introduce a new method that draws on random data generation procedures, unlike Bootstrap which is based on resampling, and Monte Carlo simulations.
|Date of creation:||05 Feb 2012|
|Date of revision:|
|Contact details of provider:|| Postal: Ludwigstraße 33, D-80539 Munich, Germany|
Web page: https://mpra.ub.uni-muenchen.de
More information through EDIRC
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.:
- Perelman, Sergio & Santín, Daniel, 2009. "How to generate regularly behaved production data? A Monte Carlo experimentation on DEA scale efficiency measurement," European Journal of Operational Research, Elsevier, vol. 199(1), pages 303-310, November.
- Simar, Léopold, 2003.
"How to Improve the Performances of DEA/FDH Estimators in the Presence of Noise?,"
SFB 373 Discussion Papers
2003,33, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
- Léopold Simar, 2007. "How to improve the performances of DEA/FDH estimators in the presence of noise?," Journal of Productivity Analysis, Springer, vol. 28(3), pages 183-201, December.
- Léopold Simar & Paul W. Wilson, 1998.
"Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models,"
INFORMS, vol. 44(1), pages 49-61, January.
- Simar, L. & Wilson, P.W., . "Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models," CORE Discussion Papers RP 1304, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- SIMAR, Léopold & WILSON, Paul, 1995. "Sensitivity Analysis to Efficiency Scores : How to Bootstrap in Nonparametric Frontier Models," CORE Discussion Papers 1995043, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- H. Sherman & Joe Zhu, 2006. "Benchmarking with quality-adjusted DEA (Q-DEA) to seek lower-cost high-quality service: Evidence from a U.S.bank application," Annals of Operations Research, Springer, vol. 145(1), pages 301-319, July.
- 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.
When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:39226. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter)
If references are entirely missing, you can add them using this form.