Estimation in a simple linear regression model with measurement error
Estimation in a simple linear regression model with measurement error
复制标题
带有测量误差的简单线性回归模型的估计
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Hisayuki Tsukuma
中科院分区：
文献类型：
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作者：
Hisayuki Tsukuma
本文研究了自变量具有函数测量误差的简单线性回归模型中斜率参数的估计问题。众所周知，自变量的测量误差会导致普通最小二乘估计的偏倚。给出了在有限样本情况下减少偏倚的一般方法，并给出了一些精确的减少偏倚估计量。此外，还证明了某些截断方法可以改善普通最小二乘估计和去偏估计的均方误差。
This paper deals with the problem of estimating a slope parameter in a simple linear regression model, where independent variables have functional measurement errors. Measurement errors in independent variables, as is well known, cause biasedness of the ordinary least squares estimator. A general procedure for the bias reduction is presented in a finite sample situation, and some exact bias-reduced estimators are proposed. Also, it is shown that certain truncation procedures improve the mean square errors of the ordinary least squares and the bias-reduced estimators.