Estimation of within model parameters in regression models with a nested error structure
Estimation of within model parameters in regression models with a nested error structure
复制标题
具有嵌套误差结构的回归模型中模型内参数的估计
DOI：
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发表时间：
1992
期刊：
影响因子：
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通讯作者：
Dallas Johnson
中科院分区：
文献类型：
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作者：
G. Weerakkody;Dallas Johnson
限制随机化，类似于分裂图型实验，通常适用于为实验单位分配定量处理因子。这些限制导致实验具有嵌套误差结构。给出了回归量参数的普通最小二乘估计是一致最小方差无偏（UMVU）的充分条件。如果设计的实验能满足这些条件，分析就会简单明了。当这些条件不满足时，提出并比较了嵌套回归参数的三种不同估计方法。
Abstract Restricted randomizations, similar to those in split-plot type experiments, often are adapted to assign quantitative treatment factors to experimental units. Such restrictions result in the experiment having a nested error structure. Sufficient conditions are presented under which ordinary least squares (OLS) estimates of regressor parameters are uniformly minimum variance unbiased (UMVU). If one designs experiments so that these conditions are satisfied, the analysis is straightforward and easy. When these conditions are not met, three different estimators of nested regressor parameters are suggested and compared.