变系数部分线性模型的加权随机约束s-K估计
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引用本文:张 巍 巍.变系数部分线性模型的加权随机约束s-K估计[J].经济数学,2020,(4):159-163
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作者单位
张 巍 巍 (内蒙古农业大学 理学院内蒙古 呼和浩特 010018) 
中文摘要:研究随机约束条件下半参数变系数部分线性模型的参数估计问题,当回归模型线性部分变量存在多重共线性时,基于Profile最小二乘方法、s-K估计和加权混合估计构造参数向量的加权随机约束s-K估计,并在均方误差矩阵准则下给出新估计量优于s-K估计和加权混合估计的充要条件,最后通过蒙特卡洛数值模拟验证所提出估计量的有限样本性质.
中文关键词:变系数部分线性模型  多重共线性  随机线性约束  Profile最小二乘方法  s-K估计  均方误差矩阵
 
A Weighted Stochastic Restricted s-K Estimator in Varying-Coefficient Partially Linear Model
Abstract:The parameter estimation problem of semiparametric varying-coefficient partially linear model is studied when the linear part variables of the regression model exist multicollinearity. Based on the stochastic linear constraint, a weighted stochastic restricted s-K estimator for parameter vector is constructed by using the profile least squares method, the s-K estimation and the weighted mixed estimation. The necessary and sufficient conditions for the new estimator to be superior to the weighted mixed estimator and the s-K estimator are given out under the scalar mean square error matrix criterion. Finally, the finite sample properties of the proposed estimators are verified by Monte Carlo numerical simulation.
keywords:varying-coefficient partially linear model  multicollinearity  stochastic linear constraint  profile least squares method  s-K estimator  mean square error matrix
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