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Efficiency Performance and Dominance in Influential Subsets: An Evaluation using Fuzzy Clustering and Pair-wise Dominance

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  • Bill Seaver
  • Konstantinos Triantis
  • Barbara Hoopes

Abstract

The identification of influential subsets in efficiency studies is an ongoing research concern. Seaver et al. (1999) have demonstrated the value of using a fuzzy clustering strategy to identify influential subsets without knowing which production plans are outliers a priori. From a fuzzy clustering perspective, dominance is assessed in terms of the degree of belonging of a production plan in an influential subset. From a production point of view, dominance is assessed in terms of pair-wise comparisons. These concepts of dominance within influential subsets are illustrated using the preprint insertion data set (Girod, 1996). Copyright Kluwer Academic Publishers 2004

Suggested Citation

  • 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.
    • Handle: RePEc:kap:jproda:v:21:y:2004:i:2:p:201-220
    DOI: 10.1023/B:PROD.0000016873.63406.90
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    References listed on IDEAS

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    1. Henry Tulkens & Philippe Eeckaut, 2006. "Nonparametric Efficiency, Progress and Regress Measures For Panel Data: Methodological Aspects," Springer Books, in: Parkash Chander & Jacques Drèze & C. Knox Lovell & Jack Mintz (ed.), Public goods, environmental externalities and fiscal competition, chapter 0, pages 395-429, Springer.
    2. Pastor, Jesus T. & Ruiz, Jose L. & Sirvent, Inmaculada, 1999. "A statistical test for detecting influential observations in DEA," European Journal of Operational Research, Elsevier, vol. 115(3), pages 542-554, June.
    3. Wilson, Paul W, 1993. "Detecting Outliers in Deterministic Nonparametric Frontier Models with Multiple Outputs," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(3), pages 319-323, July.
    4. Dusansky, Richard & Wilson, Paul W., 1995. "On the relative efficiency of alternative modes of producing a public sector output: The case of the developmentally disabled," European Journal of Operational Research, Elsevier, vol. 80(3), pages 608-618, February.
    5. Zhu, Joe, 1996. "Robustness of the efficient DMUs in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 90(3), pages 451-460, May.
    6. Seaver, Bill L & Triantis, Konstantinos P, 1989. "The Implications of Using Messy Data to Estimate Production-Frontier-Based Technical Efficiency Measures," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(1), pages 49-59, January.
    7. Barrett, Bruce E. & Gray, J. Brian, 1997. "Leverage, residual, and interaction diagnostics for subsets of cases in least squares regression," Computational Statistics & Data Analysis, Elsevier, vol. 26(1), pages 39-52, November.
    8. Konstantinos Triantis & Philippe Eeckaut, 2000. "Fuzzy Pair-wise Dominance and Implications for Technical Efficiency Performance Assessment," Journal of Productivity Analysis, Springer, vol. 13(3), pages 207-230, May.
    9. 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.
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    Cited by:

    1. Ashley Pedigo & William Seaver & Agricola Odoi, 2011. "Identifying Unique Neighborhood Characteristics to Guide Health Planning for Stroke and Heart Attack: Fuzzy Cluster and Discriminant Analyses Approaches," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-11, July.
    2. Adel Hatami-Marbini & Per J. Agrell & Hirofumi Fukuyama & Kobra Gholami & Pegah Khoshnevis, 2017. "The role of multiplier bounds in fuzzy data envelopment analysis," Annals of Operations Research, Springer, vol. 250(1), pages 249-276, March.

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