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dc.contributor.authorPuig Oriol, Xavier
dc.contributor.authorGinebra Molins, Josep
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.date.accessioned2018-10-03T06:21:36Z
dc.date.available2019-06-06T02:30:46Z
dc.date.issued2018
dc.identifier.citationPuig, X., Ginebra, J. Outlier detection for multivariate categorical data. "Quality and reliability engineering international", 2018, vol. 34, núm. 7, p. 1400-1412.
dc.identifier.issn0748-8017
dc.identifier.urihttp://hdl.handle.net/2117/121784
dc.descriptionThis is an Accepted Manuscript of an article published by Taylor & Francis in “ Quality and Reliability Engineering International ” on 06th June 2018, available online: https://onlinelibrary.wiley.com/doi/abs/10.1002/qre.2339
dc.description.abstractThe detection of outlying rows in a contingency table is tackled from a Bayesian perspective, by adapting the framework adopted by Box and Tiao for normal models to multinomial models with random effects. The solution assumes a 2–component mixture model of 2 multinomial continuous mixtures for them, one for the nonoutlier rows and the second one for the outlier rows. The method starts by estimating the distributional characteristics of nonoutlier rows, and then it does cluster analysis to identify which rows belong to the outlier group and which do not. The method applies to any type of contingency table, and in particular, it could be used on the analysis of multivariate categorical control charts. Here, the use of the method is illustrated through a simulated example and by applying it to help identify heterogeneities of style among the acts in the plays of the First Folio edition of Shakespeare drama
dc.format.extent13 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
dc.subject.lcshMathematical statistics
dc.subject.lcshData analytics
dc.titleOutlier detection for multivariate categorical data
dc.typeArticle
dc.subject.lemacEstadística matemàtica
dc.subject.lemacAnàlisi de dades -- Models matemàtics
dc.contributor.groupUniversitat Politècnica de Catalunya. ADBD - Anàlisi de Dades Complexes per a les Decisions Empresarials
dc.identifier.doi10.1002/qre.2339
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://onlinelibrary.wiley.com/doi/abs/10.1002/qre.2339
dc.rights.accessOpen Access
drac.iddocument23183826
dc.description.versionPostprint (author's final draft)
upcommons.citation.authorPuig, X.; Ginebra, J.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameQuality and reliability engineering international
upcommons.citation.volume34
upcommons.citation.number7
upcommons.citation.startingPage1400
upcommons.citation.endingPage1412


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