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[Untitled]

Authors :
Léopold Simar
Source :
Journal of Productivity Analysis. 20:391-424
Publication Year :
2003
Publisher :
Springer Science and Business Media LLC, 2003.

Abstract

In frontier analysis, most of the nonparametric approaches (DEA, FDH) are based on envelopment ideas which suppose that with probability one, all the observed units belong to the attainable set. In these "deterministic'' frontier models, statistical theory is now mostly available (Simar and Wilson, 2000a). In the presence of superefficient outliers, envelopment estimators could behave dramatically since they are very sensitive to extreme observations. Some recent results from Cazals et al. (2002) on robust nonparametric frontier estimators may be used in order to detect outliers by de. ning a new DEA/FDH "deterministic'' type estimator which does not envelop all the data points and so is more robust to extreme data points. In this paper, we summarize the main results of Cazals et al. (2002) and we show how this tool can be used for detecting outliers when using the classical DEA/FDH estimators or any parametric techniques. We propose a methodology implementing the tool and we illustrate through some numerical examples with simulated and real data. The method should be used in a first step, as an exploratory data analysis, before using any frontier estimation.

Details

ISSN :
0895562X
Volume :
20
Database :
OpenAIRE
Journal :
Journal of Productivity Analysis
Accession number :
edsair.doi...........421345fd8e5092eb281c1af7a7dbe8f2
Full Text :
https://doi.org/10.1023/a:1027308001925