Covariance based moment equations for improved variance component estimation
Covariance based moment equations for improved variance component estimation
复制标题
用于改进方差分量估计的基于协方差的矩方程
DOI：
10.1080/02331888.2022.2144856
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发表时间：
2022
期刊：
影响因子：
1.9
通讯作者：
S. Sugasawa
中科院分区：
文献类型：
--
作者：
S. Chaudhuri;T. Kubokawa; S. Sugasawa
嵌套误差回归模型的方差分量的方差分析估计通常是通过残差方差建立在矩方程的基础上的。我们考虑矩方程与残差协方差和构造改进的方差估计。所提出的估计有封闭形式的解析表达式，这使得易于计算。此外，他们被证明是一致的，渐近无偏的，和强大的随机效应分布的选择。这些估计有可比的，往往更好的性能比许多传统的方差分量估计，如Prasad-Rao，最大似然估计，并限制最大似然估计几乎所有类型的样本分配。他们的改进性能证明分析，以及通过详细的模拟研究和应用到真实的数据集。
ANOVA-based estimators of variance components for nested-error regression models are always constructed based on moment equations through residual variance. We consider moment equations associated with residual covariance and construct improved ANOVA-based estimators. The proposed estimators have closed-form analytic expressions, which enables easy computation. Moreover, they are shown to be consistent, asymptotically unbiased, and robust to the choice of distribution of the random effects. These estimators have comparable and often better performances than many traditional estimators of variance components like the Prasad-Rao, maximum likelihood, and the restricted maximum likelihood estimators for almost all kinds of sample allocations. Their improved performances are demonstrated analytically as well as through detailed simulation studies and applications to real data sets.