Parametric Empirical Bayes Point and Interval Estimation in Small Area Estimation from Complex Surveys

复杂调查小区域估计中的参数经验贝叶斯点和区间估计

• 批准号: 9705574
• 负责人: Parthasarathi Lahiri
• 金额: $ 6.51万
• 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: 1997
• 资助国家: 美国
• 起止时间: 1997-08-15 至 2001-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9705574&HistoricalAwards=false
• 关键词: Parametric; Empirical; Bayes; Point; Interval

This collaborative research investigates various problems associated with parametric empirical Bayes point and interval estimation and measure of uncertainty of a parametric empirical Bayes small-area estimator when the data are obtained from a complex survey. In addition, the investigators will conduct three real world applications of parametric empirical Bayes analysis: (1) Estimation of U.S. Census undercount; (2) Estimation of the median income of four-person families for fifty U.S. States and the District of Columbia; and (3) Estimation of the unemployment rates for fifty U.S. states and the District of Columbia. There is a growing demand by many U.S. and overseas federal agencies to produce reliable small area statistics for various subgroups of a population. Usual design-based survey estimators are not suitable for this purpose since a typical sample survey being designed for a large population contains very little information regarding the sub-populations or small areas of interest. The problem is generally referred to as a small-area (domain) estimation problem in the sample survey literature. Development of reliable small-area statistics and suitable measures of uncertainty using information from complex surveys is extremely important. This research will advance small-area estimation methods.

这项协作研究调查了与参数经验的估计值以及量度的各种问题，以及从复杂的调查获得数据时，参数经验贝叶斯小区域估计量的不确定性。 此外，研究人员将对参数经验贝叶斯分析进行三种现实世界的应用：（1）估计美国人口普查案件； （2）估计美国五十个州和哥伦比亚特区的四人家庭的中位收入； （3）估计五十个美国和哥伦比亚特区的失业率。 许多美国和海外联邦机构的需求不断增长，以生成可靠的小面积统计数据，以针对人口的各个小组。 通常的基于设计的调查估计量不适合此目的，因为为大量人群设计的典型样本调查所包含的有关子人群或较小感兴趣领域的信息很少。 在样本调查文献中，该问题通常称为小区域（域）估计问题。 使用复杂调查中的信息开发可靠的小区域统计数据和适当的不确定性措施非常重要。 这项研究将推进小区域估计方法。