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

Bahari, Ali R. and Emrouznejad, Ali Influential DMUs and outlier detection in data envelopment analysis with an application to health care. Annals of Operations Research, 223 (1), pp. 95-108.

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.

Publication DOI: https://doi.org/10.1007/s10479-014-1604-8
Divisions: Aston Business School > Operations & information management
Aston Business School > Operations & information management research group
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Uncontrolled Keywords: data envelopment analysis,bootstrapping,outlier detection,influential DMU,hospital efficiency,DEA,Management Science and Operations Research,Decision Sciences(all)

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