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Forward search outlier detection in data envelopment analysis

Tiziano Bellini ()

European Journal of Operational Research, 2012, vol. 216, issue 1, 200-207

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.

Keywords: Data envelopment analysis (DEA); Super-efficiency; Forward search; Outlier detection (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:216:y:2012:i:1:p:200-207

DOI: 10.1016/j.ejor.2011.07.023

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European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

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