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多阶段抽样设计中简化的无偏方差估计AI 解读

All unequal probability selection methods of two first-stage units per stratum are shown to permit the use of a grouping device, leading to simplified unbiased variance estimators in multistage sampling. These variance estimators avoid the calculation of within first-stage components with a high probability. Several desirable properties of the grouping method are established. Empirical results on efficiencies of ‘grouping’ estimators and stabilities of corresponding variance…

多阶段抽样设计中的简化无偏方差估计AI 解读

All unequal probability selection methods of two first-stage units per stratum are shown to permit the use of a grouping device, leading to simplified unbiased variance estimators in multistage sampling. These variance estimators avoid the calculation of within first-stage components with a high probability. Several desirable properties of the grouping method are established. Empirical results on efficiencies of ‘grouping’ estimators and stabilities of corresponding variance…