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

这项合作研究探讨了各种问题与参数经验贝叶斯点和区间估计和测量的不确定性参数经验贝叶斯小面积估计时，从一个复杂的调查数据。 此外，研究人员将进行参数经验贝叶斯分析的三个真实的世界应用：（1）估计美国人口普查不足;（2）估计美国50个州和哥伦比亚特区的四口之家的收入中位数;（3）估计美国50个州和哥伦比亚特区的失业率。 许多美国和海外的联邦机构越来越需要为人口的各个子群体提供可靠的小区域统计数据。 基于抽样设计的调查估计量不适合这一目的，因为为大人口设计的典型抽样调查包含很少的关于子人口或感兴趣的小区域的信息。 在抽样调查文献中，该问题通常被称为小区域（域）估计问题。 利用复杂调查的信息，编制可靠的小地区统计数据和适当的不确定性衡量标准极为重要。 该研究将促进小区域估计方法的发展。

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.