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2013•Journal of MathematicsOpen access

ALMOST UNBIASED RIDGE ESTIMATOR FOR A MIXED-EFFECT COEFFICIENT LINEAR MODEL

Binghua Jiang

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Abstract

In the paper, we consider a mixed-effect coefficient linear model with the repeatedly measured data. Using the ridge method, the almost unbiased ridge estimator (AURE) of the model are given. It is proved that the AURE excels the ridge estimator (RE) under a mean square error (MSE), and the optimal value for the biased parameter is established by minimum MSE.

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What this paper is about

In the paper, we consider a mixed-effect coefficient linear model with the repeatedly measured data. Using the ridge method, the almost unbiased ridge estimator (AURE) of the model are given. It is proved that the AURE excels the ridge estimator (RE) under a mean square error (MSE), and the optimal value for the biased parameter is established by minimum MSE.

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Available abstract

In the paper, we consider a mixed-effect coefficient linear model with the repeatedly measured data. Using the ridge method, the almost unbiased ridge estimator (AURE) of the model are given. It is proved that the AURE excels the ridge estimator (RE) under a mean square error (MSE), and the optimal value for the biased parameter is established by minimum MSE.

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

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