Outlier detection by robust principal components analysis

Caroni, C

dc.contributor.author	Caroni, C	en
dc.date.accessioned	2014-03-01T01:48:41Z
dc.date.available	2014-03-01T01:48:41Z
dc.date.issued	1999	en
dc.identifier.issn	03610926	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/25560
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-28544436933&partnerID=40&md5=7c00e07b541948d39035f959f6b33cdc	en
dc.subject	Multivariate outliers	en
dc.subject	Outlier tests	en
dc.subject	Principal components analysis	en
dc.subject	Robust estimation	en
dc.title	Outlier detection by robust principal components analysis	en
heal.type	journalArticle	en
heal.publicationDate	1999	en
heal.abstract	The robust principal components analysis (RPCA) introduced by Campbell (Applied Statistics 1980, 29, 231-237) provides in addition to robust versions of the usual output of a principal components analysis, weights for the contribution of each point to the robust estimation of each component. Low weights may thus be used to indicate outliers. The present simulation study provides critical values for testing the kth smallest weight in the RPCA of a sample of n p-dimensional vectors, under the null hypothesis of a multivariate normal distribution. The cases p=2(2)10, 15, 20 for n=20, 30, 40, 50, 75, 100 subject to n≥p/2, are examined, with k≤√n. Copyright © 2000 by Marcel Dekker, Inc.	en
heal.journalName	Communications in Statistics - Theory and Methods	en
dc.identifier.volume	29	en
dc.identifier.issue	1	en
dc.identifier.spage	139	en
dc.identifier.epage	151	en