2013Unpublished venueRequires access

The superiority of Bayes linear unbiased minimum variance estimator with respect to ridge estimator

Liu Xieji

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Abstract

The superiority of Bayes linear unbiased minimum variance(BLUMV)estimator with respect to ridge estimator of unknown parameter was studied in terms of the mean square error matrix criterion.Under balanced loss risk function criterion,The superiority of BLUMV estimator over ridge estimator of unknown parameter was investigated,and BLUMV estimator and least square estimator can converge to the same one under a certain condition.

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The superiority of Bayes linear unbiased minimum variance(BLUMV)estimator with respect to ridge estimator of unknown parameter was studied in terms of the mean square error matrix criterion.Under balanced loss risk function criterion,The superiority of BLUMV estimator over ridge estimator of unknown parameter was investigated,and BLUMV estimator and least square estimator can converge to the same one under a certain condition.

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Available abstract

The superiority of Bayes linear unbiased minimum variance(BLUMV)estimator with respect to ridge estimator of unknown parameter was studied in terms of the mean square error matrix criterion.Under balanced loss risk function criterion,The superiority of BLUMV estimator over ridge estimator of unknown parameter was investigated,and BLUMV estimator and least square estimator can converge to the same one under a certain condition.

Key concepts: Minimum-variance unbiased estimator, Bias of an estimator, Stein's unbiased risk estimate, Efficient estimator, Mean squared error, Consistent estimator, Estimator, Mathematics

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