A New Bound for the Euclidean Norm of the Difference Between the Least Squares and the Best Linear Unbiased Estimators
针对一般线性模型中期望向量的最小二乘估计与最佳线性无偏估计之差的欧几里得范数，建立了一个新的上界，该界比已有结果更简单且适用于任意秩的散布矩阵。
A new bound is established for the Euclidean norm of the difference between the least squares estimator and the best linear unbiased estimator of the vector of expectations in the general linear model. The bound is valid regardless of the rank of the dispersion matrix and is expressed in substantially simpler terms than the bounds given earlier by Haberman and by Baksalary and Kala.