Posterior moments of scale parameters in elliptical regression models

Jacek Osiewalski and Mark Steel

UC3M Working papers. Economics from Universidad Carlos III de Madrid. Departamento de Economía

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.

Keywords: Multivariate; elliptical; data; densities; Bayesian; analysis; Unbiased; estimation (search for similar items in EconPapers)
Date: 1992-02
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Persistent link: https://EconPapers.repec.org/RePEc:cte:werepe:10879

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