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dc.contributor.authorRIANI Marcoen_GB
dc.contributor.authorCERIOLI Andreaen_GB
dc.contributor.authorPERROTTA DOMENICOen_GB
dc.contributor.authorTORTI FRANCESCAen_GB
dc.date.accessioned2010-02-25T15:39:58Z-
dc.date.available2008-08-20en_GB
dc.date.available2010-02-25T15:39:58Z-
dc.date.created2008-08-20en_GB
dc.date.issued2008en_GB
dc.date.submitted2008-04-25en_GB
dc.identifier.citationAtti della XLIV Riunione Scientifica p. 163-170en_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC45085-
dc.description.abstractThe goal of this paper is to describe a semi-automatic approach to outlier detection and clustering through the forward search (Atkinson and Riani, 2000; Atkinson, Riani and Cerioli, 2004).After an overview of the basic principles of the forward search, we concentrate on the identification of clusters of points coming from different regression models. The method is motivated in the context of fraud detection in external trade data sets, where robust fitting of mixtures of regression lines is an important open issue (see Bishop, 2006, for non-robust methods). Our tools for outlier detection and clustering are developed from forward plots of residuals computed from searches with random starting points. We also address a number of challenging issues, including selection of the number of groups. We make use of simulation envelopes and distributional results to precisely identify the outliers and the clusters. The performance of the algorithm is shown on several European trade data sets relevant for fraud detection problems selected by the antifraud services of the European Commission and of the Member States.en_GB
dc.description.sponsorshipJRC.G.2-Support to external securityen_GB
dc.format.mediumPrinteden_GB
dc.languageENGen_GB
dc.publisherCooperativa Libraria Editrice Universitaria di Padova - CLEUPen_GB
dc.relation.ispartofseriesJRC45085en_GB
dc.titleRobust Methods for Complex Dataen_GB
dc.typeArticles in periodicals and booksen_GB
JRC Directorate:Space, Security and Migration

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