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Gibbs sampling will fail in outlier problems with strong masking

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorJustel, Ana
dc.contributor.authorPeña, Daniel
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned2009-05-13T07:48:19Z
dc.date.available2009-05-13T07:48:19Z
dc.date.issued1995-06
dc.description.abstractThis paper discusses the convergence of the Gibbs sampling algorithm when it is applied to the problem of outlier detection in regression models. Given any vector of initial conditions, theoretically, the algorithm converges to the true posterior distribution. However, the speed of convergence may slow down in a high dimensional parameter space where the parameters are highly correlated. We show that the effect of the leverage in regression models makes very difficult the convergence of the Gibbs sampling algorithm in sets of data with strong masking. The problem is illustrated in several examples.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10016/4203
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries1995-21-05
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaEstadística
dc.subject.otherBayesian analysis
dc.subject.otherLeverage
dc.subject.otherLinear regression
dc.subject.otherScale contamination
dc.titleGibbs sampling will fail in outlier problems with strong masking
dc.typeworking paper*
dspace.entity.typePublication
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