The Linear Least-Squares Prediction Approach to Two-Stage Sampling
The Linear Least-Squares Prediction Approach to Two-Stage Sampling
复制标题
两级采样的线性最小二乘预测方法
DOI：
10.1080/01621459.1976.10481542
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发表时间：
1976
影响因子：
3.7
通讯作者：
R. Royall
中科院分区：
文献类型：
--
作者：
R. Royall
摘要 线性最小二乘预测方法应用于有限总体两阶段抽样的一些问题。给出了在有限总体的一般线性“超总体”模型下给出最优（BLU）估计量及其误差方差的定理。然后将该定理应用于描述许多群体的模型，这些群体的元素自然地分组为簇。接下来，概率模型被用来分析各种传统的估计量和理论提出的作为传统估计量的替代品的某些估计量。考虑了设计问题以及回归模型失败的一些后果。
Abstract The linear least-squares prediction approach is applied to some problems in two-stage sampling from finite populations. A theorem giving the optimal (BLU) estimator and its error-variance under a general linear “superpopulation” model for a finite population is stated. This theorem is then applied to a model describing many populations whose elements are grouped naturally in clusters. Next, the probability model is used to analyze various conventional estimators and certain estimators suggested by the theory as alternatives to the conventional ones. Problems of design are considered, as are some consequences of regression-model failure.