Posterior moments of scale parameters in 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 2011-04-25T17:44:13Z
dc.date.available 2011-04-25T17:44:13Z
dc.date.issued 1992-02
dc.identifier.issn 2340-5031
dc.identifier.uri http://hdl.handle.net/10016/10879
dc.description.abstract In the general multivariate elliptical class of data densities we define a scalar precision parameter r through a normalization of the scale matrix V. Using the improper prior on r which preserves the results under Normality for all other parameters and prediction, we consider the posterior moments of r. For the subclass of scale mixtures of Normals we derive the Bayesian counterpart to a sampling theory result concerning uniformly minimum variance unbiased estimation of 7. 2 • If the sampling variance exists, we single out the common variance factor i' as the scalar multiplying V in this sampling variance. Moments of i' are examined for various elliptical subclasses and a sampling theory result regarding its unbiased estimation is mirrored.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Economics
dc.relation.ispartofseries 92-05
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Multivariate elliptical data densities
dc.subject.other Bayesian analysis
dc.subject.other Unbiased estimation
dc.title Posterior moments of scale parameters in elliptical regression models
dc.type workingPaper
dc.subject.eciencia Economía
dc.rights.accessRights openAccess
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