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Outlier detection in data envelopment analysis: an analysis of jackknifing

Author

Listed:
  • J Ondrich

    (Syracuse University)

  • J Ruggiero

    (University of Dayton)

Abstract

This paper analyzes the resampling technique of jackknifing and its capability of detecting outliers in data envelopment analysis. It is well recognized that measured efficiency is sensitive to outliers; recent research has employed resampling techniques to estimate standard deviations in an attempt to handle outliers. Using jackknifing, each observation other than the decision making unit under analysis is deleted from the sample once and the resulting linear program is solved, leading to a distribution of efficiency estimates. From this distribution, standard deviations and confidence intervals are derived. Two types of outliers can be distinguished conceptually: those belonging to the production possibility set that are efficient, and those that do not belong but appear to due to statistical noise. This paper argues that calculation of the standard deviation is not meaningful because it is not possible to distinguish empirically between the two types of outliers.

Suggested Citation

  • J Ondrich & J Ruggiero, 2002. "Outlier detection in data envelopment analysis: an analysis of jackknifing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(3), pages 342-346, March.
    • Handle: RePEc:pal:jorsoc:v:53:y:2002:i:3:d:10.1057_palgrave.jors.2601290
    DOI: 10.1057/palgrave.jors.2601290
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    Citations

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    Cited by:

    1. Khezrimotlagh, Dariush & Cook, Wade D. & Zhu, Joe, 2020. "A nonparametric framework to detect outliers in estimating production frontiers," European Journal of Operational Research, Elsevier, vol. 286(1), pages 375-388.
    2. Mohammed Al-Siyabi & Gholam R. Amin & Shekar Bose & Hussein Al-Masroori, 2019. "Peer-judgment risk minimization using DEA cross-evaluation with an application in fishery," Annals of Operations Research, Springer, vol. 274(1), pages 39-55, March.
    3. Christian von Hirschhausen & Astrid Cullmann, 2008. "Next Stop: Restructuring?: A Nonparametric Efficiency Analysis of German Public Transport Companies," Discussion Papers of DIW Berlin 831, DIW Berlin, German Institute for Economic Research.
    4. Stead, Alexander D. & Wheat, Phill & Greene, William H., 2023. "Robust maximum likelihood estimation of stochastic frontier models," European Journal of Operational Research, Elsevier, vol. 309(1), pages 188-201.
    5. Marcel Clermont & Julia Schaefer, 2019. "Identification of Outliers in Data Envelopment Analysis," Schmalenbach Business Review, Springer;Schmalenbach-Gesellschaft, vol. 71(4), pages 475-496, October.
    6. Magdalena Kapelko & Alfons Oude Lansink, 2015. "An international comparison of productivity change in the textile and clothing industry: a bootstrapped Malmquist index approach," Empirical Economics, Springer, vol. 48(4), pages 1499-1523, June.
    7. Congcong Yang & Alfred Taudes & Guozhi Dong, 2017. "Efficiency analysis of European Freight Villages: three peers for benchmarking," 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. 25(1), pages 91-122, March.

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    Keywords

    data envelopment analysis; statistics;

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