The Prediction Approach to Robust Variance Estimation in Two-Stage Cluster Sampling
针对两阶段整群抽样中第一阶段抽样比例较小的情况，基于预测模型推导了估计总体总量或比率时误差方差的稳健统计量，适用于已知或仅知样本群大小的场景。
Abstract Robust statistics are derived for estimating the error variance when a finite population total or a ratio of totals is estimated from a two-stage cluster sample in which the first-stage sampling fraction is small. The analysis is based on prediction (superpopulation) models that allow for very general correlation structure within clusters. Results are obtained for when all cluster sizes are known and for when sizes are known only for sample clusters. Ratio, regression, and mean-of-ratios statistics are in the class of population total estimators considered.