Collaborative research: Topics in Small Area Estimation

术语“小区域”或“局部区域”通常指一个小的地理区域，如县、市、人口普查区域或学区。它还可以指社会人口领域，例如一个大的地理区域内的特定年龄-性别-种族群体。近年来，小面积估算已成为一个日益重要的话题，因为许多公共和私人机构都需要可靠的小面积估算，以作出有用的决策。该项目旨在解决小区域估计的几个重要方面。在这方面要解决的一个基本问题是，如何将调查权重(通常是人口中不同单位的选择概率的倒数)与模型结合使用，以得出有意义的小区域估计值。虽然已经提出了许多独占的基于模型的小区域估计器，但设计辅助的基于模型的小区域估计器一直非常稀少。我们的目标是为一类非常一般的分布获得这样的估计值。该方法将用于找出美国不同县的贫困学龄儿童的比例和数量。对于许多联邦机构来说，这是一个非常重要的问题，特别是对人口普查局来说。这项研究的另一个方面是通过结合两个或更多旨在估计相同兴趣数量的调查的结果来获得小面积估计。这一程序的典型应用包括结合基于当前人口调查(CPS)和人口普查局新推出的美国社区调查(ACS)的数据。《人口普查报告》的目的是在2010年取代十年一次的详细人口普查表格。最后，开发贝叶斯方法来检测有限总体抽样中的异常值，特别是在小区域估计的背景下。本研究的更广泛的影响在于它旨在实现调查方法与调查实践的接口。作为一个直接的例子，研究结果直接影响到小地区的收入和贫困估计，以及通过结合CPS和ACS等两项调查的估计得出的小地区估计。国家卫生统计中心的工作人员也应该对这一发现感兴趣，他们对估计不同种族的未参保人口比例、参加医疗补助的人数比例等感兴趣。

The term "small area" or "local area" usually refers to a small geographic area, such as a county, municipality, a census tract, or a school district. It can also refer to socio-demographic domains, such as a specific age-sex-race group within a large geographic area. Small area estimation has become a topic of growing importance in recent years because of the need for reliable small area estimates by many agencies, both public and private, for making useful policy decisions. This project is aimed at addressing several important aspects of small area estimation. One basic question to address in this context is how to use the survey weights (usually inverses of the selection probabilities of the different units in the population) in conjunction with models to arrive at meaningful small area estimators. While many exclusive model-based small area estimators have been proposed, design-assisted model-based small area estimators have been very sparse. The goal is to obtain such estimators for a very general class of distributions. The method will be used to find the proportion and the number of poor school-age children in different counties of the United States. This is a very important problem for many Federal agencies, especially for the Bureau of the Census. Another aspect of this research is to obtain small area estimates by combining results from two or more surveys designed to estimate the same quantity of interest. A typical application of this procedure consists of combining data based on the Current Population Survey (CPS) and the newly introduced American Community Survey (ACS) of the Bureau of the Census. The ACS is intended to replace the decennial census long form in the year 2010. Finally, Bayesian methods will be developed for detecting outliers in finite population sampling, especially in the context of small area estimation.The broader impact of this research is that it aims to achieve an interface between survey methodology and survey practice. As an immediate example, the research findings have direct bearing on small area income and poverty estimation as well as small area estimation by combining estimates from two surveys such as the CPS and the ACS. The findings also should be of interest to staff at the National Center for Health Statistics who are interested in estimating the proportion of uninsured people of different ethnicities, proportion of people under Medicaid, and so on.