摘要
对由于包含多余回归自变量而导致的错误指定线性回归模型,本文导出了回归系数的最小二乘估计,普通混合估计以及随机约束Liu估计,并在均方误差矩阵准则下对这三个估计的优良性进行了比较,给出了随机约束Liu估计优于最小二乘估计和普通混合估计的充要条件.此外,对它们所对应的经典预测值的优良性也进行了讨论.
In this paper,we firstly derived the expressions of the well-known ordinary least square estimator（OLSE）,the ordinary mixed estimator（OME）introduced by Theil and Golberger（1961）and the stochastic restricted Liu estimator（SRLE）proposed by Yang and Xu（2007）under misspecification due to inclusion of some superfluous variables.Then,performances of these estimators under misspecification are examined.In particular,necessary and sufficient conditions for the superiority of the SRLE over the OLSE and OME with respect to the mean squared error matrix（MSEM） criterion are derived.Furthermore,superiority of the corresponding predictors of these estimators are also investigated.
出处
《应用概率统计》
CSCD
北大核心
2012年第2期123-133,共11页
Chinese Journal of Applied Probability and Statistics
基金
supported by National Natural Science Foundation of China(11001286)
Natural Science Foundation Project of CQ CSTC(2009BB6189)
关键词
错误指定线性模型
随机约束
随机约束Liu估计
均方误差矩阵
Misspecified linear model
stochastic restrictions
stochastic restricted Liu estimator
mean squared error matrix
