Empirical Bayes and Hierarchical Bayes Analysis of Small Area Means in Complex Surveys

复杂调查中小面积均值的经验贝叶斯和分层贝叶斯分析

• 批准号: 9511202
• 负责人: Parthasarathi Lahiri
• 金额: $ 6.38万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-08-15 至 1998-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9511202&HistoricalAwards=false
• 关键词: Empirical; Bayes; Hierarchical; Analysis; Small

The sampling design and sample size of most large scale national surveys are usually determined so as to produce a national estimate of a parameter of interest with a desired level of precision. Quite often there is a need to produce similar estimates with the same level of precision for certain subnational regions (such as states or counties). However, the small samples associated with subnational regions do not produce reliable estimates. Problems of this kind are known as small area estimation problems. Reliable small area statistics are needed by various federal and local government agencies for policy making and the allocation of funds. The empirical Bayes (EB) and hierarchical Bayes (HB) methods have been widely used to produce small area statistics. These methods use explicit hierarchical models to borrow strength from related resources. This research extends the empirical Bayes (EB) and hierarchical Bayes (HB) methods to address the following issues in small area estimation: (1) Measurement errors in survey responses; (2) Small area statistics from a dual-frame sampling design; and (3) Estimation of small sample means under exponential power distributions.

大多数大规模国家调查的抽样设计和抽样规模通常是为了以所需的精确度对有关参数作出国家估计而确定的。 经常需要为某些国家以下区域（如州或县）编制具有相同精确度的类似估计数。 然而，与国家以下区域有关的小样本并不能产生可靠的估计数。 这类问题被称为小区域估计问题。 各个联邦和地方政府机构需要可靠的小地区统计数据来制定政策和分配资金。 经验贝叶斯（EB）和分层贝叶斯（HB）方法已被广泛用于产生小区域统计。 这些方法使用明确的层次模型，从相关资源中借用力量。 本研究扩展经验贝氏（EB）和阶层贝氏（HB）方法，以解决以下问题，在小区域估计：（1）测量误差的调查响应;（2）小区域统计的双框架抽样设计;（3）估计小样本平均指数功率分布。