淡江大學機構典藏:Item 987654321/69191
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    题名: Estimators which are Uniformly better than the James-Stein Estimator
    作者: Pal, Nabendu;Lin, Jyh-jiuan
    贡献者: 淡江大學統計學系
    日期: 1997
    上传时间: 2011-10-23 16:33:12 (UTC+8)
    摘要: 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.
    關聯: Calcutta Statistical Association Bulletin 47(187-188), pp.167-179
    DOI: 10.1177%2F0008068319970304
    显示于类别:[統計學系暨研究所] 期刊論文

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