One important issue in DEA which has been studied by many DEA researchers is the efficiency sensitivity to perturbations in the data. Some DEA sensitivity studies focus on the sensitivity of DEA results to the variable and model selection, e.g., Ahn and Seiford (1993). Most of the DEA sensitivity analysis studies focus on the misspecification of efficiency classification of a test DMU. However, note that DEA is an extremal method in the sense that all extreme points are characterized as efficient. If data entry errors occur for various DMUs, the resulting isoquant may vary substantially. We say that the calculated frontiers of DEA models are stable if the frontier DMUs that determine the DEA frontier remain on the frontier after particular data perturbations are made.


Data Perturbation Efficiency Classification Spreadsheet Model Inefficient DMUs Frontier Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Joe Zhu
    • 1
  1. 1.Worcester Polytechnic InstituteUSA

Personalised recommendations