Estimators which are Uniformly better than the James-Stein Estimator

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Title:	Estimators which are Uniformly better than the James-Stein Estimator
Authors:	Pal, Nabendu;Lin, Jyh-jiuan
Contributors:	淡江大學統計學系
Date:	1997
Issue Date:	2011-10-23 16:33:12 (UTC+8)
Abstract:	Assume i.i.d. observations are available from a p-dimensional multivariate normal distribution with an unknown mean vector μ and an unknown p .d. diaper- . sion matrix ∑. Here we address the problem of mean estimation in a decision theoretic setup. It is well known that the unbiased as well as the maximum likelihood estimator of μ is inadmissible when p ≤ 3 and is dominated by the famous James-Stein estimator (JSE). There are a few estimators which are better than the JSE reported in the literature, but in this paper we derive wide classes of estimators uniformly better than the JSE. We use some of these estimators for further risk study.
Relation:	Calcutta Statistical Association Bulletin 47(187-188), pp.167-179
DOI:	10.1177%2F0008068319970304
Appears in Collections:	[Graduate Institute & Department of Statistics] Journal Article

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