Generalized Bayes minimax estimators of the mean of multivariate normal distribution with unknown variance

We construct a broad class of generalized Bayes minimax estimators of the mean of a multivariate normal distribution with covariance equal to [sigma]2Ip, with [sigma]2 unknown, and under the invariant loss ||[delta](X)-[theta]||2/[sigma]2. Examples that illustrate the theory are given. Most notably it is shown that a hierarchical version of the multivariate Student-t prior yields a Bayes minimax estimate.

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Year of publication:	2008
Authors:	Wells, Martin T. ; Zhou, Gongfu
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 99.2008, 10, p. 2208-2220
Publisher:	Elsevier
Subject:	primary \| 62C10 secondary \| 62A15 Generalized Bayes estimate Integration by parts Minimax estimate Multivariate normal mean Invariant loss Unknown variance Weakly differentiable function

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005221360