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The Impact of Outliers and Leverage Points for Technical Efficiency Measurement Using High Breakdown Procedures

Author

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  • Bill L. Seaver

    (Department of Statistics, The University of Tennessee, Knoxville, Tennessee 37996-0532)

  • Konstantinos P. Triantis

    (Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Northern Virginia Graduate Center, 2990 Telestar Court, Falls Church, Virginia 22042)

Abstract

Given that most data used for production studies have not been accumulated for such purposes, it is important that the quantitative tools for messy data which can affect the accuracy of computed technical efficiency measures be found. In this study, high breakdown robust methods are used in conjunction with a robust distance measure defined relative to the minimum volume ellipsoid estimator. The standardized robust residuals from the high breakdown estimators and the robust distance measures are used to statistically and graphically depict both multivariate outliers and leverage points. Once these points are found, their relationship to those observations that exhibit strong technically efficient or inefficient behavior, scale inefficiency and/or unusual production characteristics is analyzed for three linerboard manufacturing facilities. Additionally, the impact of the outliers and leverage points on the estimated least squares coefficients which are used by the corrected ordinary least squares methodology to compute the full-frontier technical efficiency measures is explored. Finally, a sensitivity analysis of the impact of outliers and leverage points on the computed linear programming based technical efficiency measures is presented.

Suggested Citation

  • Bill L. Seaver & Konstantinos P. Triantis, 1995. "The Impact of Outliers and Leverage Points for Technical Efficiency Measurement Using High Breakdown Procedures," Management Science, INFORMS, vol. 41(6), pages 937-956, June.
    • Handle: RePEc:inm:ormnsc:v:41:y:1995:i:6:p:937-956
    DOI: 10.1287/mnsc.41.6.937
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    Citations

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    Cited by:

    1. Pavur, Robert, 2002. "A comparative study of the effect of the position of outliers on classical and nontraditional approaches to the two-group classification problem," European Journal of Operational Research, Elsevier, vol. 136(3), pages 603-615, February.
    2. Marcus Porembski & Kristina Breitenstein & Paul Alpar, 2005. "Visualizing Efficiency and Reference Relations in Data Envelopment Analysis with an Application to the Branches of a German Bank," Journal of Productivity Analysis, Springer, vol. 23(2), pages 203-221, May.
    3. Triantis, Konstantinos & Sarangi, Sudipta & Kuchta, Dorota, 2003. "Fuzzy pair-wise dominance and fuzzy indices: An evaluation of productive performance," European Journal of Operational Research, Elsevier, vol. 144(2), pages 412-428, January.
    4. Sullivan, Joe H. & Warkentin, Merrill & Wallace, Linda, 2021. "So many ways for assessing outliers: What really works and does it matter?," Journal of Business Research, Elsevier, vol. 132(C), pages 530-543.
    5. Bill Seaver & Konstantinos Triantis & Barbara Hoopes, 2004. "Efficiency Performance and Dominance in Influential Subsets: An Evaluation using Fuzzy Clustering and Pair-wise Dominance," Journal of Productivity Analysis, Springer, vol. 21(2), pages 201-220, March.
    6. Chumpitaz, Ruben & Kerstens, Kristiaan & Paparoidamis, Nicholas & Staat, Matthias, 2010. "Comparing efficiency across markets: An extension and critique of the methodology," European Journal of Operational Research, Elsevier, vol. 205(3), pages 719-728, September.
    7. Tsekouras, Kostas & Chatzistamoulou, Nikos & Kounetas, Kostas, 2017. "Productive performance, technology heterogeneity and hierarchies: Who to compare with whom," International Journal of Production Economics, Elsevier, vol. 193(C), pages 465-478.
    8. Athanassopoulos, Antreas D. & Lambroukos, Nikos & Seiford, Lawrence, 1999. "Data envelopment scenario analysis for setting targets to electricity generating plants," European Journal of Operational Research, Elsevier, vol. 115(3), pages 413-428, June.
    9. Farnè, Matteo & Vouldis, Angelos T., 2018. "A methodology for automised outlier detection in high-dimensional datasets: an application to euro area banks' supervisory data," Working Paper Series 2171, European Central Bank.
    10. Timothy F. Slaper & Nicholas R. Hart & Tanya J. Hall & Michael F. Thompson, 2011. "The Index of Innovation: A New Tool for Regional Analysis," Economic Development Quarterly, , vol. 25(1), pages 36-53, February.
    11. 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.
    12. 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.
    13. Zhuo, Shuaihe, 2018. "Local influence analysis of stochastic frontier estimation: A case-weights perturbation approach," Economics Letters, Elsevier, vol. 164(C), pages 79-81.
    14. de Sousa, Maria da Conceição Sampaio & Cribari-Neto, Francisco & Stosic, Borko D., 2005. "Explaining DEA Technical Efficiency Scores in an Outlier Corrected Environment: The Case of Public Services in Brazilian Municipalities," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 25(2), November.
    15. Uwe Jensen, 2005. "Misspecification Preferred: The Sensitivity of Inefficiency Rankings," Journal of Productivity Analysis, Springer, vol. 23(2), pages 223-244, May.

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