Detecting outliers in frontier models : a simple approach.
(2001) , 37 pages
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- Abstract
- 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 super-efficient outliers, envelopment estimators could behave dramatically since they are very sensitive to extreme observations. Some recent results from Cazals, Florens and Simar (2002) on robust nonparametric frontier estimators may be used in order to detect outliers by defining 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, Florens and Simar (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.
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Citations
Simar, L. (2001). Detecting outliers in frontier models : a simple approach. (STAT Discussion Papers 0146). https://hdl.handle.net/2078.5/35481