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Comparing probabilistic methods for outlier detection

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dc.affiliation.dptoUC3M. Departamento de EconomĆ­aes
dc.contributor.authorPeƱa, Daniel
dc.contributor.authorGuttman, Irwin
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de EconomĆ­a
dc.date.accessioned2008-08-25T10:10:33Z
dc.date.available2008-08-25T10:10:33Z
dc.date.issued1992-07
dc.description.abstractThis paper compares the use of two posterior probability methods to deal with outliers in linear models. We show that putting together diagnostics that come from the mean-shift and variance-shift models yields a procedure that seems to be more effective than the use of probabilities computed from the posterior distributions of actual realized residuals. The relation of the suggested procedure to the use of a certain predictive distribution for diagnostics is derived.
dc.format.mimetypeapplication/pdf
dc.identifier.issn2340-5031
dc.identifier.urihttps://hdl.handle.net/10016/2841
dc.language.isoeng
dc.relation.ispartofseriesWorking Papers
dc.relation.ispartofseries1992-32
dc.rightsAtribuciĆ³n-NoComercial-SinDerivadas 3.0 EspaƱa
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaEconomĆ­a
dc.subject.otherDiagnostic
dc.subject.otherPosterior and Predictive distributions
dc.subject.otherLeverage
dc.subject.otherLinear models
dc.titleComparing probabilistic methods for outlier detection
dc.typeworking paper*
dspace.entity.typePublication
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