並列摘要
A compromise estimator employing poststratification is proposed for small area estimation. The estimator strikes a balance to deal with the assumption of similarity within a poststratum and small number of observations in subareas constructed by poststratification. Small sample properties are derived for the estimator from simple random samples. By assuming that the probability that poststratum size equal to zero is negiligibly small, the estimator can be justified in Bayesian terms based on an inherent superpopulation model. The generalization of poststratification from the simple random sampling to complex design is discussed.
延伸閱讀
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