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Forward search outlier detection in data envelopment analysis

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  • Bellini, Tiziano

Abstract

In this paper we tackle the problem of outlier detection in data envelopment analysis (DEA). We propose a procedure where we merge the super-efficiency DEA and the forward search. Since DEA provides efficiency scores which are not parameters to fit the model to the data, we introduce a distance, to be monitored along the search. This distance is obtained through the integration of a regression model and the super-efficiency DEA. We simulate a Cobb–Douglas production function and we compare the super-efficiency DEA and the forward search analysis in both uncontaminated and contaminated settings. For inference about outliers, we exploit envelopes obtained through Monte Carlo simulations.

Suggested Citation

  • Bellini, Tiziano, 2012. "Forward search outlier detection in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 216(1), pages 200-207.
    • Handle: RePEc:eee:ejores:v:216:y:2012:i:1:p:200-207
    DOI: 10.1016/j.ejor.2011.07.023
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    References listed on IDEAS

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    4. 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.
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