Component risk in multiparameter estimation |
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Authors: | Khursheed Alam Amitava Mitra |
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Institution: | (1) Clemson University, Clemson, USA;(2) Auburn University, Auburn, USA |
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Abstract: | Summary For estimating the mean of ap-variate normal distribution under a quadratic loss, a class of estimators, known as Stein's estimators, is known to dominate
the maximum likelihood estimator (MLE) forp≧3. But, whereas the risk of the MLE has the same value, equal to a constant, for each component, the maximum component risk
of Stein's estimator is large for large values ofp. Certain modification of Stein's rule has been proposed in the literature for reducing the maximum component risk. In this
paper, a new rule is given for reducing the maximum component risk. The new rule yields larger reduction in the maximum component
risk, compared to its competitor. |
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Keywords: | Multivariate normal distribution maximum likelihood risk Stein's rule |
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