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误差协方差矩阵未知时线性模型中最小二乘法的最优性
Optimal properties of the least-squares coefficient estimator in the linear model with scalar error covariance matrix are shown to carry over to the case where the error covariance matrix has a distribution in a class which includes distributions proposed as representing vague prior information.
误差协方差矩阵未知时线性模型中最小二乘的最优性
Optimal properties of the least-squares coefficient estimator in the linear model with scalar error covariance matrix are shown to carry over to the case where the error covariance matrix has a distribution in a class which includes distributions proposed as representing vague prior information.
普通最小二乘估计为最优线性无偏估计的充要条件
It is shown that in a standard linear regression model ordinary least-squares estimators are best linear unbiased if and only if the errors have the same variance and the same nonnegative coefficient of correlation between each pair.