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http://hdl.handle.net/10071/13940
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acessibilidade
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dc.contributor.authorMarques, A.-
dc.contributor.authorFerreira, A. S.-
dc.contributor.authorCardoso, M. G. M. S.-
dc.date.accessioned2017-07-11T14:30:08Z-
dc.date.available2017-07-11T14:30:08Z-
dc.date.issued2013-
dc.identifier.issn1896-3811por
dc.identifier.urihttps://ciencia.iscte-iul.pt/id/ci-pub-15551-
dc.identifier.urihttp://hdl.handle.net/10071/13940-
dc.description.abstractIn Discrete Discriminant Analysis one often has to deal with dimensionality problems. In fact, even a moderate number of explanatory variables leads to an enormous number of possible states (outcomes) when compared to the number of objects under study, as occurs particularly in the social sciences, humanities and health-related elds. As a consequence, classi cation or discriminant models may exhibit poor performance due to the large number of parameters to be estimated. In the present paper, we discuss variable selection techniques which aim to address the issue of dimensionality. We speci cally perform classi cation using a combined model approach. In this setting, variable selection is particularly pertinent, enabling the handling of degrees of freedom and reducing computational cost.por
dc.language.isoengpor
dc.publisherWalter de Gruyter GmbHpor
dc.rightsopenAccesspor
dc.subjectCombining modelspor
dc.subjectDiscrete Discriminant Analysispor
dc.subjectVariable selectionpor
dc.titleSelection of variables in Discrete Discriminant Analysispor
dc.typearticleen_US
dc.publicationstatusPublicadopor
dc.peerreviewedyespor
dc.relation.publisherversionThe definitive version is available at: http://dx.doi.org/10.2478/bile-2013-0013por
dc.journalBiometrical Letterspor
dc.distributionInternacionalpor
dc.volume50por
dc.number1por
degois.publication.issue1por
degois.publication.titleBiometrical Letterspor
dc.date.updated2017-07-11T14:28:51Z-
dc.identifier.doi10.2478/bile-2013-0013-
Appears in Collections:BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica

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