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dc.contributor.authorCabana, Elisa 
dc.contributor.authorLillo, Rosa Elvira
dc.date.accessioned2022-01-11T13:06:55Z
dc.date.available2022-01-11T13:06:55Z
dc.date.issued2021-12-30
dc.identifier.issn0169-7439es
dc.identifier.urihttp://hdl.handle.net/20.500.12761/1557
dc.description.abstractA novel discriminant analysis (DA) method is proposed, based on the robust reweighted shrinkage estimators and a robust Mahalanobis distance with an adjusted quantile as threshold. A simulation study is done to evaluate the performance of the proposed approach in comparison with the classical DA and the other robust alternatives from the literature. The approach is also illustrated using real dataset examples: a geochemical and environmental dataset known as the Kola Project and a second data containing the spectra of different cultivars of a fruit. The results show the appropriateness of the method while being computationally efficient at the same time. Additional simulations are included to show the additional benefits in outlier detection.es
dc.language.isoenges
dc.publisherElsevieres
dc.titleRobust adjusted discriminant analysis based on shrinkage with application to geochemical and environmental fieldses
dc.typejournal articlees
dc.journal.titleChemometrics and Intelligent Laboratory Systemses
dc.type.hasVersionVoRes
dc.rights.accessRightsembargoed accesses
dc.volume.number221es
dc.identifier.doi10.1016/j.chemolab.2021.104488es
dc.subject.keywordRobust discriminant analysises
dc.subject.keywordShrinkagees
dc.subject.keywordMultivariate outlierses
dc.subject.keywordKola projectes
dc.subject.keywordAdjusted quantilees
dc.description.refereedTRUEes
dc.description.statuspubes


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