Mes performance of the minimum mean squared error estimators in a linear regression model when relevant regressors are omitted
Mes performance of the minimum mean squared error estimators in a linear regression model when relevant regressors are omitted
复制标题
当相关回归量被省略时，线性回归模型中最小均方误差估计量的 Mes 性能
DOI：
10.1080/00949659808811902
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发表时间：
1998
期刊：
影响因子：
--
通讯作者：
K. Ohtani
中科院分区：
文献类型：
--
作者：
K. Ohtani
本文考虑了一个线性回归模型，当相关回归变量被忽略时。给出了可行最小均方误差（FMMSE）估计和调整FMMSE估计的均方误差（MSE）的显式表达式。通过数值计算，我们比较了FMMSE和AFMMSE估计与Stein规则（SR）和正部Stein规则（PSR）估计的MSE性能。结果表明，当模型中包含的回归变量个数（sayk 1）大于或等于8时，当有遗漏回归变量时，AFMMSE和PSR估计的均方误差性能相当;当k1 ≤5且模型误设严重时，AFMMSE估计的均方误差性能优于PSR估计.
In this paper, we consider a linear regression model when relevant regressors are omitted. We derive the explicit formulae of the mean squared errors (MSE's) of the feasible minimum MSE (FMMSE) estimator and the adjusted FMMSE (AFMMSE) estimator. By numerical evaluations, we compare the MSE performances of the FMMSE and AFMMSE estimators with those of the Stein-rule (SR) and positive-part Stein-rule (PSR) estimators. It is shown that when there are omitted regressors, the MSE performances of the AFMMSE and PSR estimators are comparable when the number of regressors included in the specified model (sayk 1) is larger than or equal to 8, and the MSE performance of the AFMMSE estimator is better than that of the PSR estimator when k 1≤5 and the model misspecification is severe.