By employing the concept of statistical curvatures, the information loss of the maximum likelihood estimator and the generalized least squares estimator is investigated.
利用统计曲率的概念，研究结构方程模型的最大似然估计量和广义最小二乘估计量的信息损失，得到了简明的结果。
This paper studies linear regression models with dependent errors, and we introduce the jackknifed least squares estimator and generalized jackknife least squares estimator.
在误差为相依的情况下，讨论了线性回归模型的刀切最小二乘估计与广义刀切最小二乘估计。
Systematically introduced parameter estimation of distributed sources on the base of models, including the maximum likelihood estimate, least squares estimator, DSPE, DISPARE, etc.
在模型基础上系统地介绍了已有分布式目标参数估计方法，包括最大似然与最小二乘算法，DSPE和DISPARE算法等。
To solve the problem, a non-quadratic objective estimator, the weighted least absolute value (WLAV) estimator, and least mean squares (LMS) estimator are proposed.
为解决这一问题，已经提出了加权最小绝对值准则、非二次准则等估计器。
To solve the problem, a non-quadratic objective estimator, the weighted least absolute value (WLAV) estimator, and least mean squares (LMS) estimator are proposed.
为解决这一问题，已经提出了加权最小绝对值准则、非二次准则等估计器。
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