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Sensitivity in data envelopment analysis using an approximate inverse matrix

Author

Listed:
  • L Neralić

    (University of Zagreb)

  • R E Wendell

    (University of Pittsburgh)

Abstract

In this paper, sensitivity analysis of the Charnes–Cooper–Rhodes model in data envelopment analysis (DEA) is studied for the case of perturbation of all outputs and of all inputs of an efficient decision-making unit (DMU). Using an approximate inverse of the perturbed optimal basis matrix, an approximate preservation of efficiency for an efficient DMU under these perturbations is considered. Sufficient conditions for an efficient DMU to preserve its efficiency are obtained in that case. An illustrative example is provided.

Suggested Citation

  • L Neralić & R E Wendell, 2004. "Sensitivity in data envelopment analysis using an approximate inverse matrix," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(11), pages 1187-1193, November.
    • Handle: RePEc:pal:jorsoc:v:55:y:2004:i:11:d:10.1057_palgrave.jors.2601785
    DOI: 10.1057/palgrave.jors.2601785
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    References listed on IDEAS

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    1. W. Cooper & Shanling Li & L. Seiford & Kaoru Tone & R. Thrall & J. Zhu, 2001. "Sensitivity and Stability Analysis in DEA: Some Recent Developments," Journal of Productivity Analysis, Springer, vol. 15(3), pages 217-246, May.
    2. Richard E. Wendell, 1985. "The Tolerance Approach to Sensitivity Analysis in Linear Programming," Management Science, INFORMS, vol. 31(5), pages 564-578, May.
    3. Seiford, Lawrence M. & Zhu, Joe, 1998. "Stability regions for maintaining efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 108(1), pages 127-139, July.
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    Citations

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    Cited by:

    1. Michael Best & Xili Zhang, 2012. "The Efficient Frontier for Weakly Correlated Assets," Computational Economics, Springer;Society for Computational Economics, vol. 40(4), pages 355-375, December.
    2. D T Barnum & J M Gleason & B Hemily & J Lin & P Wang, 2010. "Progressing from uncertainty to risk for DEA-based decisions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(10), pages 1548-1555, October.
    3. Imad Bou-Hamad & Abdel Latef Anouze & Ibrahim H. Osman, 2022. "A cognitive analytics management framework to select input and output variables for data envelopment analysis modeling of performance efficiency of banks using random forest and entropy of information," Annals of Operations Research, Springer, vol. 308(1), pages 63-92, January.
    4. Darold Barnum & John Gleason, 2011. "Measuring efficiency under fixed proportion technologies," Journal of Productivity Analysis, Springer, vol. 35(3), pages 243-262, June.
    5. Neralić, Luka & Wendell, Richard E., 2019. "Enlarging the radius of stability and stability regions in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 278(2), pages 430-441.
    6. Luka Neralić & Richard E. Wendell, 2019. "Sensitivity in DEA: an algorithmic approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(4), pages 1245-1264, December.

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