A Distribution-Free Test for Outliers

16 Pages Posted: 21 Jun 2016

See all articles by Bertrand Candelon

Bertrand Candelon

University of Maastricht - Department of Economics

Norbert Metiu

Deutsche Bundesbank

Date Written: 2013


Determining whether a data set contains one or more outliers is a challenge commonly faced in applied statistics. This paper introduces a distribution-free test for multiple outliers in data drawn from an unknown data generating process. Besides, a sequential algorithm is proposed in order to identify the outlying observations in the sample. Our methodology relies on a two-stage nonparametric bootstrap procedure. Monte Carlo experiments show that the proposed test has good asymptotic properties, even for relatively small samples and heavy tailed distributions. The new outlier detection test could be instrumental in a wide range of statistical applications. The empirical performance of the test is illustrated by means of two examples in the fields of aeronautics and macroeconomics.

Keywords: bootstrap, mode testing, nonparametric statistics, outlier detection

JEL Classification: C14

Suggested Citation

Candelon, Bertrand and Metiu, Norbert, A Distribution-Free Test for Outliers (2013). Bundesbank Discussion Paper No. 02/2013, Available at SSRN: https://ssrn.com/abstract=2796894 or http://dx.doi.org/10.2139/ssrn.2796894

Bertrand Candelon (Contact Author)

University of Maastricht - Department of Economics ( email )

P.O. Box 616
Maastricht, 6200 MD

Norbert Metiu

Deutsche Bundesbank ( email )

Wilhelm-Epstein-Str. 14
Frankfurt/Main, 60431

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