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Influential DMUs and outlier detection in data envelopment analysis with an application to health care

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  • Ali Bahari
  • Ali Emrouznejad

Abstract

This paper explains some drawbacks on previous approaches for detecting influential observations in deterministic nonparametric data envelopment analysis models as developed by Yang et al. (Annals of Operations Research 173:89–103, 2010 ). For example efficiency scores and relative entropies obtained in this model are unimportant to outlier detection and the empirical distribution of all estimated relative entropies is not a Monte-Carlo approximation. In this paper we developed a new method to detect whether a specific DMU is truly influential and a statistical test has been applied to determine the significance level. An application for measuring efficiency of hospitals is used to show the superiority of this method that leads to significant advancements in outlier detection. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Ali Bahari & Ali Emrouznejad, 2014. "Influential DMUs and outlier detection in data envelopment analysis with an application to health care," Annals of Operations Research, Springer, vol. 223(1), pages 95-108, December.
    • Handle: RePEc:spr:annopr:v:223:y:2014:i:1:p:95-108:10.1007/s10479-014-1604-8
    DOI: 10.1007/s10479-014-1604-8
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    Cited by:

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    3. Lee, Jiyoung & Kim, Chulyeon & Choi, Gyunghyun, 2019. "Exploring data envelopment analysis for measuring collaborated innovation efficiency of small and medium-sized enterprises in Korea," European Journal of Operational Research, Elsevier, vol. 278(2), pages 533-545.
    4. 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.

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