Best Invariant Unbiased Estimators for the Mean Squared Error of Variance Component Estimators
该文推导了可表示为独立平方和线性组合的任意无偏或有偏估计量的均方误差的无偏估计量，并证明在经典平衡方差分量模型中，该估计量是ANOVA估计量方差和非负最小偏估计量均方误差的最佳不变无偏估计量。
Abstract An unbiased estimator is derived for the mean squared error of any unbiased or biased estimator that is expressible as a linear combination of independent sums of squares. Further, it is shown that, for the classical balanced variance component models, this estimator is the best invariant unbiased estimator for the variance of the ANOVA estimator and for the mean squared error of the nonnegative minimum biased estimator. As an example, the balanced one-way classification model with random effects is considered.