Bayesian Inference for Finite Population Parameters in Multistage Cluster Sampling
针对多阶段抽样调查，描述了有限总体元素线性函数的贝叶斯预测推断，证明后验均值在均方误差最小化方面具有最优频率性质，并详细分析了三阶段抽样的特例及先验参数变化的影响。
Abstract Assuming a model appropriate for many multistage sample surveys, Bayesian predictive inference for a general linear function, ω, of the finite population elements is described. In a broad class of linear estimators of ω, the posterior mean, E″(ω), of ω is shown to have the optimal frequentist property of minimal bounded mean squared error. For the special case of three-stage sampling, E″(ω) is described in detail. Also presented are the results of an investigation of the effect on inferences of alteration of the values of some parameters in the prior distribution.