Gibbs sampling will fail in outlier problems with strong masking

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dc.contributor.author Justel, Ana
dc.contributor.author Peña, Daniel
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2009-05-13T07:48:19Z
dc.date.available 2009-05-13T07:48:19Z
dc.date.issued 1995-06
dc.identifier.uri http://hdl.handle.net/10016/4203
dc.description.abstract This 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.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries 1995-21-05
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Bayesian analysis
dc.subject.other Leverage
dc.subject.other Linear regression
dc.subject.other Scale contamination
dc.title Gibbs sampling will fail in outlier problems with strong masking
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
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