2011Journal of Shandong UniversityRequires access

The superiority of the Bayes linear unbiased minimum Variance estimator under balanced loss function

Miao Bai-qi

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Abstract

The superiority of the Bayes linear unbiased minimum variance(BLUMV) estimator with respect to the optimally weighted least square(OWLS) estimator of unknown parameter was studied in terms of the balanced loss risk function criterion,and the two estimators can converge to the same one under a certain condition.The superiority of the BLUMV estimator over the OWLS estimator was studied under predictive Pitman closeness(PRPC) criterion.

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The superiority of the Bayes linear unbiased minimum variance(BLUMV) estimator with respect to the optimally weighted least square(OWLS) estimator of unknown parameter was studied in terms of the balanced loss risk function criterion,and the two estimators can converge to the same one under a certain condition.The superiority of the BLUMV estimator over the OWLS estimator was studied under predictive Pitman closeness(PRPC) criterion.

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Available abstract

The superiority of the Bayes linear unbiased minimum variance(BLUMV) estimator with respect to the optimally weighted least square(OWLS) estimator of unknown parameter was studied in terms of the balanced loss risk function criterion,and the two estimators can converge to the same one under a certain condition.The superiority of the BLUMV estimator over the OWLS estimator was studied under predictive Pitman closeness(PRPC) criterion.

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

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