Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/68604
Authors: 
Candelon, Bertrand
Metiu, Norbert
Year of Publication: 
2013
Series/Report no.: 
Discussion Paper, Deutsche Bundesbank 02/2013
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.
Subjects: 
bootstrap
mode testing
nonparametric statistics
outlier detection
JEL: 
C14
ISBN: 
978-3-86558-882-1
Document Type: 
Working Paper

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