...摘要： 在均方误差矩阵准则下研究了未知参数的Bayes线性无偏最小方差(Bayes linear unbiased minimum variance estimator,BLUMV)估计相对于最小二乘(least square,LS)估计的优良性,并讨论了3种不同相对效率的界.
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线性无偏最小方差估计 LUMV
线性无偏最小方差估计与最优加权最小二乘估计是线性模型下两种最常用的估计方法。
The linear unbiased minimum variance estimate and the optimally weighted least squares estimate are two of the most popular estimation methods for a linear model.
在给定的线性模型下，讨论了最优加权最小二乘估计与线性无偏最小方差估计性能比较。
The discussion on the property comparison between optimally weighted LS estimate and linear unbiased minimum variance estimate for a linear model is presented.
在线性无偏最小方差估计准则下，推导出了该离散化后所得系统的全局最优递推状态估计算法。
In the sense of linear unbiased minimum variance estimation, a global optimal recursive state estimation algorithm for this discretized linear system is proposed.
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