A distribution-free test for outliers
Bertrand Candelon and
Norbert Metiu
No 02/2013, Discussion Papers from Deutsche Bundesbank
Abstract:
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 (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:bubdps:022013
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