Posterior moments of scale parameters in elliptical regression models

Osiewalski, Jacek; Steel, Mark F.J.

Publication:
Posterior moments of scale parameters in elliptical regression models

dc.affiliation.dpto	UC3M. Departamento de Economía	es
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.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.identifier.issn	2340-5031
dc.identifier.uri	https://hdl.handle.net/10016/10879
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.accessRights	open access
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.eciencia	Economía
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	working paper	*
dspace.entity.type	Publication