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。

This paper studies linear regression models with dependent errors, and we introduce the jackknifed least squares estimator and generalized jackknife least squares estimator.

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而广义移动最小二乘近似要求近似函数及其导数在所有节点处的误差的平方和最小。

However, the generalized moving least squares approximation makes require least squares approximation with regard to functional and its derivative value on all nodes.

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对通常的模型匹配解法进行了扩展， 并据此引入广义最小方差控制思想， 提出一种模型跟踪和误差调节的新方法。

The model matching solution for LMFC is expanded. On the basis of this solution, a new algorithm for model following and error adjustment is presented.

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