On the design-consistency property of hierarchical bayes estimators in finite population sampling

• 成果类型: Article
• 署名作者: Lahiri, P.; Mukherjee, Kanchan
• 署名单位: University System of Maryland; University of Maryland College Park; University of Liverpool
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053606000001262
• 发表日期: 2007
• 页码: 724-737
• 关键词: prediction; models; error

摘要

We obtain a limit of a hierarchical Bayes estimator of a finite population mean when the sample size is large. The limit is in the sense of ordinary calculus, where the sample observations are treated as fixed quantities. Our result suggests a simple way to correct the hierarchical Bayes estimator to achieve design-consistency, a well-known property in the traditional randomization approach to finite population sampling. We also suggest three different measures of uncertainty of our proposed estimator.