Improved Estimators for Ratios of Variance Components
针对平衡单因素随机效应模型中方差分量比值的估计问题，证明了ML、REML和贝叶斯模态估计量不可容许，并提出了一个优于它们的估计量，同时引入两个自适应估计量，均显著改善了均方误差。
The problem of estimating a ratio of variance components in the balanced one-way random effects model is considered. It is shown that in terms of mean squared error, the ML, REML (or truncated ANOVA), and Bayes modal estimators (using the noninformative prior) are inadmissible. An estimator that dominates all three is derived. Two other estimators that are adaptive in nature are also introduced. The new estimators are shown to possess much-improved mean squared error properties. The results easily extend to balanced higher-way random or mixed effects models.