Title: Fitting Mixtures of Regression Lines with the Forward Search
Publisher: IOS Press
Publication Year: 2008
JRC N°: JRC42676
ISBN: 978-1-58603-898-4
ISSN: 1874-6268
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC42676
DOI: 10.3233/978-1-58603-898-4-271
Type: Articles in periodicals and books
Abstract: The forward search is a powerful method for detecting unidentified subsets and masked outliers and for determining their effect on models fitted to the data. This paper describes a semi-automatic approach to outlier detection and clustering through the forward search. Its main contribution is the development of a novel technique for the identification of clusters of points coming from different regression models. The method was motivated by fraud detection in foreign trade data as reported by the Member States of the European Union. We also address the challenging issue of selecting the number of groups. The performance of the algorithm is shown through an application to a specific bivariate trade data set. The applicability of the method on more complex and large data sets is commented in the paper.
JRC Directorate:Space, Security and Migration

Files in This Item:
There are no files associated with this item.

Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.