The linear weighted regression model is one of the models studied in many articles in recent years.
权回归模型是近年来文献中较多涉及的一种。
Geographically weighted regression(GWR), on the other hand, is a simple, but useful new technique for the analysis of spatial nonstationarity.
GWR是一种简单、有效的技术，可以反映参数在不同空间的空间非稳定性。
Thirdly, a learning method based on Locally Weighted Regression is proposed to forecast the motion of the ball, especially after the ball bump into wall.
提出一种基于局部加权回归预测球运动轨迹的学习方法，尤其是球与边界碰撞后的运动轨迹。
For the linear weighted regression model, this paper defines a new relative efficiency, gives its lower bound and also discusses its relation to other three efficiencies.
考虑加权回归模型，定义了一种新的相对效率，并给出了相对效率的下界，同时讨论了新的相对效率与其它三种已有相对效率的关系。
Results show that the proposed weighted regression calibration method is the most efficient and that the standard errors estimated using a bootstrap procedure are satisfactory.
对于参数估计量的标准差，我们则是利用拔靴法来估计，其结果的表现也与仿真的标准差很接近。
Weighted least squares regression.
加权最小二乘回归。
Based on the locally kernel weighted least squares fit of the nonparametric regression models, this paper presents the nonparametric testing method for nonlinear cointegration.
本文基于非参数回归模型的局部核权最小二乘法提出变量间非线性协整的一种非参数检验方法。
Model parameters are computed using a weighted linear regression technology according to the different impacts of rate point to estimation of model parameters.
利用率点对模型参数估计的影响强弱，使用一种加权的线性回归模型参数估计算法。
Based on the local kernal weighted least squared fit of the nonparametric and additive regression model, this paper presents the nonparametric testing method for nonlinear cointegration.
本文基于非参数可加回归模型的局部核权最小二乘法提出变量间非线性协整的一种非参数检验方法。
The randomly weighted least square estimator (RWLSE) for the parametric component in semi-parametric regression models was mainly discussed.
主要考虑了同方差型的半参数线性回归模型中参数的随机加权最小二乘估计（RWLSE）。
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。
Fan J and Gijbels I gave the asymptotic normality of local polynomial regression estimation in dependent time series, where the weighted function is bounded.
对相依时间序列数据，在一定的条件下已有人证明了局部多项式加权回归系数估计服从渐近正态分布，其中核函数是有界的。
The paper suggests an approach, utilizing the weighted polynomial regression for the best fitting of the variogram about spherical model or two-order nested spherical model.
本文提出了一种用加权多项式回归对球状模型和二级套合球状模型的变差函数进行最优拟合的方法。
The regression of spherical model and its nugget structure of theoretic variogram is discussed and the weighted linear programming method is proposed in this paper.
对理论变异函数球状模型及其套合结构拟合这一问题作了探讨，提出了加权线性规划拟合法。
The calculating formulae of weighted optimum curve regression model parameter given in this paper possesses high speed of parameter correcting, high precision of fitting and obvious effect.
本文给出的带权优化曲线回归模型参数计算公式，修正参数速度快，拟合精度高，效果显著。
By means of weighted subordination degree this model weakens the influence of distinct abnormal data affecting upon the regression straight line and...
该模型通过隶属度加权来削弱个别异常数据对回归直线的影响，从而达到提高回归方程稳定性的目的。
Consequently, the learning mechanism of the proposed approach is much easier than the robust support vector regression networks (RSVRNs) approach and the weighted LS-SVMR approach.
我们所提出的方法在整个学习架构上要比强健式支援向量机网路与权重式最小平方支援向量机回归法更简易。
This paper gives weighted least squares estimate and the method to choose optimum weighted function for linear regression model.
本文给出了基于极径法球面测量的加权最小二乘估计模型，并依据该模型进行测量采样点布局的优化设计。
This paper gives weighted least squares estimate and the method to choose optimum weighted function for linear regression model.
本文给出了基于极径法球面测量的加权最小二乘估计模型，并依据该模型进行测量采样点布局的优化设计。
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