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

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.