Robust detection of multiple outliers in grouped multivariate data

Caroni, C; Billor, N

dc.contributor.author	Caroni, C	en
dc.contributor.author	Billor, N	en
dc.date.accessioned	2014-03-01T01:27:05Z
dc.date.available	2014-03-01T01:27:05Z
dc.date.issued	2007	en
dc.identifier.issn	0266-4763	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18331
dc.subject	BACON	en
dc.subject	Cluster analysis	en
dc.subject	Multivariate data	en
dc.subject	Outliers	en
dc.subject	Robust methods	en
dc.subject.classification	Statistics & Probability	en
dc.subject.other	COVARIANCE DETERMINANT ESTIMATOR	en
dc.subject.other	MIXTURE DISTRIBUTION	en
dc.title	Robust detection of multiple outliers in grouped multivariate data	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/02664760701592877	en
heal.identifier.secondary	http://dx.doi.org/10.1080/02664760701592877	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	Many methods have been developed for detecting multiple outliers in a single multivariate sample, but very few for the case where there may be groups in the data. We propose a method of simultaneously determining groups (as in cluster analysis) and detecting outliers, which are points that are distant from every group. Our method is an adaptation of the BACON algorithm proposed by Billor, Hadi and Velleman for the robust detection of multiple outliers in a single group of multivariate data. There are two versions of our method, depending on whether or not the groups can be assumed to have equal covariance matrices. The effectiveness of the method is illustrated by its application to two real data sets and further shown by a simulation study for different sample sizes and dimensions for 2 and 3 groups, with and without planted outliers in the data. When the number of groups is not known in advance, the algorithm could be used as a robust method of cluster analysis, by running it for various numbers of groups and choosing the best solution.	en
heal.publisher	ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD	en
heal.journalName	Journal of Applied Statistics	en
dc.identifier.doi	10.1080/02664760701592877	en
dc.identifier.isi	ISI:000253264000005	en
dc.identifier.volume	34	en
dc.identifier.issue	10	en
dc.identifier.spage	1241	en
dc.identifier.epage	1250	en