相依误差线性模型中的主成分s-K估计  

Principal Components s-K Class Estimator in the Linear Model with Correlated Errors

作　　者：周玲[1] 何道江[1] 

机构地区：[1]安徽师范大学数学计算机科学学院,安徽芜湖241003

出　　处：《吉林大学学报（理学版）》2015年第3期444-450,共7页Journal of Jilin University:Science Edition

基　　金：安徽省自然科学基金(批准号:1308085QA13)

摘　　要：为同时克服线性回归模型的自相关性和回归变量间的复共线性,通过融合主成分回归估计和s-K估计,提出一类新估计,称为主成分s-K估计;并在均方误差阵意义下,得到了这类估计分别优于广义最小二乘估计、主成分估计、r-k和s-K估计的充要条件.Monto Carlo数值模拟表明,新估计是一种同时克服自相关性和复共线性的有效方法.To combat autocorrelation in errors and multicollinearity among the regressors in linear regression model,we proposed a new estimator by combining the principal components regression（PCR）estimator and the s-Kestimator.Then necessary and sufficient conditions for the superiority of the new estimator over the GLS,the PCR,the r-k and the s-K estimators were derived by the mean squared error matrix criterion.Finally,a Monte Carlo simulation study was carried out to investigate the performance of the proposed estimator.

关 键 词：自相关性 复共线性 主成分回归估计 s-K估计 均方误差阵 

分 类 号：O212.2[理学—概率论与数理统计]