Collaborative research: Topics in Small Area Estimation

合作研究：小区域估计主题

• 批准号: 0318184
• 负责人: Tapabrata Maiti
• 金额: $ 16万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0318184&HistoricalAwards=false
• 关键词: Collaborative; research; Topics; Small; Area

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.

术语“小区域”或“局部区域”通常是指一个小的地理区域，如县、市、人口普查区或学区。 它也可以指社会人口统计学领域，如一个大的地理区域内的特定年龄-性别-种族群体。 近年来，由于许多公共和私人机构需要可靠的小面积估计，以便做出有用的政策决定，因此小面积估计已成为一个日益重要的主题。 该项目旨在解决小面积估算的几个重要方面。 在这方面要解决的一个基本问题是如何使用调查权重（通常是人口中不同单位的选择概率的倒数）结合模型得出有意义的小面积估计数。虽然已经提出了许多排他性的基于模型的小面积估计器，设计辅助的基于模型的小面积估计器已经非常稀疏。我们的目标是获得这样的估计一个非常普遍的一类分布。 该方法将被用来找出美国不同县贫困学龄儿童的比例和数量。 这对许多联邦机构来说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两个以上旨在估计相同数量的感兴趣的调查结果来获得小面积估计。 这一程序的一个典型应用包括将基于当前人口调查（CPS）和人口普查局新引入的美国社区调查（ACS）的数据相结合。 ACS打算在2010年取代十年一次的人口普查长表。 最后，贝叶斯方法将被开发用于在有限总体抽样中检测离群值，特别是在小面积估计的背景下，这项研究的更广泛的影响是，它旨在实现调查方法和调查实践之间的接口。 作为一个直接的例子，研究结果直接关系到小地区的收入和贫困估计以及小地区估计相结合的估计，从两个调查，如CPS和ACS。 国家卫生统计中心的工作人员也应该对这些发现感兴趣，他们有兴趣估计不同种族的未保险人群的比例，医疗补助人群的比例等。