An Outlier Detection Methodology with Consideration for an Inefficient Frontier

Johnson, A.L. and L.F. McGinnis, 2008. “Outlier Detection in Two-Stage Semiparametric DEA Models.” European Journal of Operational Research 187(2): 629-635.

27 Pages Posted: 3 Aug 2006 Last revised: 23 Feb 2018

See all articles by Andrew L Johnson

Andrew L Johnson

Texas A&M University

Leon McGinnis

Georgia Institute of Technology - The H. Milton Stewart School of Industrial & Systems Engineering (ISyE)

Date Written: July 1, 2006

Abstract

In the use of peer group data to assess individual, typical or best practice performance, the effective detection of outliers is critical for achieving useful results, particularly for two-stage analyses. In the DEA-related literature, prior work on this issue has focused on the efficient frontier as a basis for detecting outliers. An iterative approach to deal with the potential for one outlier to mask the presence of another has been proposed but not demonstrated. This paper proposes using both the efficient frontier and the inefficient frontier to identify outliers and thereby improve the accuracy of second stage results in two-stage nonparametric analysis. The iterative outlier detection approach is implemented in a leave-one-out method using both the efficient frontier and the inefficient frontier and demonstrated in a two-stage semi-parametric bootstrapping analysis of a classic data set. The results show that the conclusions drawn can be different when outlier identification includes consideration of the inefficient frontier.

Keywords: Data envelopment analysis, productivity, inefficient frontier, outlier detection

Suggested Citation

Johnson, Andrew L and McGinnis, Leon, An Outlier Detection Methodology with Consideration for an Inefficient Frontier (July 1, 2006). Johnson, A.L. and L.F. McGinnis, 2008. “Outlier Detection in Two-Stage Semiparametric DEA Models.” European Journal of Operational Research 187(2): 629-635. , Available at SSRN: https://ssrn.com/abstract=920720 or http://dx.doi.org/10.2139/ssrn.920720

Andrew L Johnson (Contact Author)

Texas A&M University ( email )

4033 Emerging Technologies Building
College Station, Texas 77843-3131
College Station, TX 77843-4353
United States

HOME PAGE: http://www.andyjohnson.guru

Leon McGinnis

Georgia Institute of Technology - The H. Milton Stewart School of Industrial & Systems Engineering (ISyE) ( email )

765 Ferst Drive
Atlanta, GA 30332-0205
United States

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