Bayesian marginal equivalence of elliptical regression models

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dc.contributor.author Osiewalski, Jacek
dc.contributor.author Steel, Mark F.J.
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Economía
dc.date.accessioned 2011-04-29T11:10:00Z
dc.date.available 2011-04-29T11:10:00Z
dc.date.issued 1992-02
dc.identifier.issn 2340-5031
dc.identifier.uri http://hdl.handle.net/10016/10950
dc.description.abstract The use of proper prior densities in regression models with multivariate non-Normal elliptical error distributions is examined when the scale matrix is known up to a precision factor T, treated as a nuisance parameter. Marginally equivalent models preserve the convenient predictive and posterior results on the parameter of interest B obtained in the reference case of the Normal model and its conditionally natural conjugate gamma prior. Prior densities inducing this property are derived for two special cases of non-Normal elliptical densities representing very different patterns of tail behavior. In a linear framework, so-called semi-conjugate prior structures are defined as leading to marginal equivalence to a Normal data density with a fully natural conjugate prior.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Economics
dc.relation.ispartofseries 92-04
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Multivariate elliptical data densities
dc.subject.other Proper priors
dc.subject.other Model robustness
dc.subject.other Student t density
dc.title Bayesian marginal equivalence of elliptical regression models
dc.type workingPaper
dc.subject.eciencia Economía
dc.rights.accessRights openAccess
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