平衡损失函数下Bayes线性无偏最小方差估计的优良性

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

在平衡损失风险函数准则下研究了未知参数的Bayes线性无偏最小方差(BLUMV)估计相对于最优加权最小二乘(OWLS)估计的优良性,并导出在一定条件下二者趋于一致。在PRPC(predictive Pitman closeness criteri-on)准则下研究了BLUMV估计相对于OWLS估计的优良性。

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.