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2009•Unpublished venueRequires access

The superiority of Bayes linear unbiased minimum variance estimator in the linear models

Miao Bai-qi

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Abstract

The superiority of Bayes linear unbiased minimum variance(BLUMV) estimator with respect to least square(LS) estimator of unknown parameters was studied in terms of the mean square error matrix criterion,and the bounds of three relative efficiencies were obtained respectively.The superiority of the BLUMV estimator over LS estimator was studied in terms of the predictive Pitman closeness(PRPC) criterion.

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What this paper is about

The superiority of Bayes linear unbiased minimum variance(BLUMV) estimator with respect to least square(LS) estimator of unknown parameters was studied in terms of the mean square error matrix criterion,and the bounds of three relative efficiencies were obtained respectively.The superiority of the BLUMV estimator over LS estimator was studied in terms of the predictive Pitman closeness(PRPC) criterion.

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Available abstract

The superiority of Bayes linear unbiased minimum variance(BLUMV) estimator with respect to least square(LS) estimator of unknown parameters was studied in terms of the mean square error matrix criterion,and the bounds of three relative efficiencies were obtained respectively.The superiority of the BLUMV estimator over LS estimator was studied in terms of the predictive Pitman closeness(PRPC) criterion.

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

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