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Sensitivity and stability of efficiency classifications in Data Envelopment Analysis

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Abstract

A new technique for assessing the sensitivity and stability of efficiency classifications in Data Envelopment Analysis (DEA) is presented. Here developed for the ratio (CCR) model, this technique extends easily to other DEA variants. An organization's input-outut vector serves as the center for a cell within which the organization's classification remains unchanged under perturbations of the data. For the l 1, l ∞ and generalized l ∞ norms, the radius of the maximal cell can be computed using linear programming formulations. This radius can be interpreted as a measure of the classification's stability, especially with respect to errors in the data.

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Authors and Affiliations

  1. Center for Cybernetic Studies, CBA 5.202, The University of Texas at Austin, 78712-1177, Austin, TX

    Abraham Charnes

  2. Center for Cybernetic Studies, CBA 5.202, The University of Texas at Austin, 78712-1177, Austin, TX

    John J. Rousseau

  3. Department of Information Systems and Management Sciences, The University of Texas at Arlington, 76019, Arlington, TX

    John H. Semple

  4. Center for Cybernetic Studies, CBA 5.202, The University of Texas at Austin, 78712-1177, Austin, TX

    John H. Semple

Authors
  1. Abraham Charnes
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  2. John J. Rousseau
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  3. John H. Semple
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Abraham Charnes passed away December 19, 1992.

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Charnes, A., Rousseau, J.J. & Semple, J.H. Sensitivity and stability of efficiency classifications in Data Envelopment Analysis. J Prod Anal 7, 5–18 (1996). https://doi.org/10.1007/BF00158473

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