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.
在这种情况下，最优加权最小二乘估计变成关于观测和输入的非线性估计，且与线性最小方差估计不可比。
In this case optimally weighted LS estimate is not a linear estimate of a parameter given input and observation anymore and can not be compared with linear minimum variance estimate.
在信号处理、控制和通讯等技术领域，常常使用线性最小方差估计和最优加权最小二乘估计对参数作出估计。
Linear minimum variance estimate and optimally weighted LS estimate are often used in many fields such as signal processing, control and communications.
给出了线性回归模型中的加权最小二乘估计以及最优权数的选择。
This paper gives weighted least squares estimate and the method to choose optimum weighted function for linear regression model.
给出了线性回归模型中的加权最小二乘估计以及最优权数的选择。
This paper gives weighted least squares estimate and the method to choose optimum weighted function for linear regression model.
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