Estimators which are Uniformly Better than the James-Stein Estimator
1997 ◽
Vol 47
(3-4)
◽
pp. 167-180
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Keyword(s):
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.
1982 ◽
Vol 11
(8)
◽
pp. 941-955
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2018 ◽
pp. 439
1995 ◽
Vol 24
(6)
◽
pp. 1377-1382
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1970 ◽
Vol 13
(3)
◽
pp. 391-393
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2021 ◽
Vol 6
(3)
◽
pp. 16-19
2016 ◽
Vol 30
(2)
◽
pp. 141-152