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Discriminant analysis of interval data: an assessment of parametric and distance-based approaches

dc.contributor.authorSilva, A. Pedro Duarte
dc.contributor.authorBrito, Paula
dc.date.accessioned2016-12-20T17:39:41Z
dc.date.available2016-12-20T17:39:41Z
dc.date.issued2015
dc.description.abstractBuilding on probabilistic models for interval-valued variables, parametric classification rules, based on Normal or Skew-Normal distributions, are derived for interval data. The performance of such rules is then compared with distancebased methods previously investigated. The results show that Gaussian parametric approaches outperform Skew-Normal parametric and distance-based ones in most conditions analyzed. In particular, with heterocedastic data a quadratic Gaussian rule always performs best. Moreover, restricted cases of the variance-covariance matrix lead to parsimonious rules which for small training samples in heterocedastic problems can outperform unrestricted quadratic rules, even in some cases where the model assumed by these rules is not true. These restrictions take into account the particular nature of interval data, where observations are defined by both MidPoints and Ranges, which may or may not be correlated. Under homocedastic conditions linear Gaussian rules are often the best rules, but distance-based methods may perform better in very specific conditions.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationDUARTE SILVA, A.P.; BRITO, Paula - Discriminant Analysis of Interval Data: An Assessment of Parametric and Distance-Based Approaches. Journal of Classification. ISSN 1432-1343. Vol. 32 (2015), p. 516-541pt_PT
dc.identifier.doi10.1007/s00357-015-9189-8
dc.identifier.eid84947129703
dc.identifier.eissn1432-1343
dc.identifier.issn0176-4268
dc.identifier.urihttp://hdl.handle.net/10400.14/21109
dc.identifier.wos000364974400007
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer
dc.relationNORTE-07-0124-FEDER-000059
dc.relationINESC TEC - INESC Technology and Science
dc.subjectDiscriminant analysispt_PT
dc.subjectInterval datapt_PT
dc.subjectParametric modelling of interval datapt_PT
dc.subjectSymbolic data analysispt_PT
dc.titleDiscriminant analysis of interval data: an assessment of parametric and distance-based approachespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleINESC TEC - INESC Technology and Science
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F50014%2F2013/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/PEst-OE%2FEGE%2FUI0731%2F2014/PT
oaire.citation.endPage541
oaire.citation.issue3
oaire.citation.startPage516
oaire.citation.titleJournal of Classification
oaire.citation.volume32
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream5876
person.familyNameDuarte Silva
person.familyNameBrito
person.givenNamePedro
person.givenNamePaula
person.identifierR-000-CF4
person.identifier.ciencia-id371F-636A-E2E9
person.identifier.ciencia-id0B1A-DD19-07CE
person.identifier.orcid0000-0003-1378-2403
person.identifier.orcid0000-0002-2593-8818
person.identifier.ridB-6168-2008
person.identifier.ridJ-6656-2013
person.identifier.scopus-author-id6602627961
person.identifier.scopus-author-id15025681300
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
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