Mean Squared Error Properties of Empirical Bayes Estimators in a Multivariate Random Effects General Linear Model
研究了多元一般线性模型中个体回归系数的估计，推导了经验贝叶斯估计量的均方误差矩阵，并证明其优于个体最小二乘估计，适用于需要估计所有模型参数的情况。
Abstract Estimation of an individual's regression coefficients is considered in a multivariate general linear model, where it is assumed that the individual's coefficients β k are subject to both fixed effects and random effects over different individuals. The mean squared error matrix of a natural estimator of β k is derived for any individual k, in the general situation where all parameters of the model must be estimated, and is shown to be smaller than the mean squared error matrix of the individual least squares estimator for every individual. Extension of this result to more general multiparameter estimation situations is also considered.