Small Area and Longitudinal Estimation using Information from Multiple Surveys

国家调查，如当前人口调查或国家犯罪受害情况调查，对国家一级的贫困或犯罪受害情况作出了准确的估计。 但是，这些调查没有足够的样本量，无法对诸如县或少数民族群体等“小地区”作出可靠的估计，也无法提供关于家庭暴力等只影响一小部分人口的事件的详细信息。 目前估计小地区贫穷的方法采用了行政来源的辅助资料，如通过回归法获得的税务记录。 这一方法假定行政数据没有错误;它也不纳入其他调查的信息或利用纵向信息，本项目的重点是将多项调查的信息结合起来，可能采用不同的抽样设计，以改进小地区的估计。 因此，为了估计小地区的贫困状况，CPS可以与美国社区调查和其他来源一起使用。 将开发考虑到缺失数据的多变量多级模型，利用不同调查的相关性提高小面积估计数的精确度，并将推导出估计数的理论特性。 多变量模型的修改将允许从纵向调查的信息被合并，以及，并允许从一个面板调查的纵向分析，以补充相关的横截面调查的信息。 调查员还将开发使用多个数据源进行离群值检测和稳健估计的方法。越来越多的信息可从调查和其他来源获得，联邦和地方政府以及社会科学家对小区域估计的需求也越来越大。 本研究中开发的模型将联合收割机结合不同调查的信息，并利用调查的纵向和空间方面来提高小面积估计的准确性，而无需额外的数据收集成本。 本研究的理论结果也将有助于其他应用领域，如遗传学和质量改进。 这项研究得到了方法、测量和统计计划以及联邦统计机构联盟的支持，该联盟隶属于调查和统计方法资助机会研究。

National surveys such as the Current Population Survey (CPS) or the National Crime Victimization Survey give accurate estimates of poverty or criminal victimization at the national level. These surveys do not, however, contain sufficient sample sizes to give reliable estimates by themselves for "small areas" such as counties or minority groups, or to provide detailed information about events such as domestic violence that affect only a small part of the population. Current methods for estimating poverty in small areas incorporate auxiliary information from administrative sources such as tax records through regression. This approach assumes that the administrative data are without errors; it also does not incorporate information from other surveys or make use of longitudinal information.This project focuses on combining information from multiple surveys, with possibly different sampling designs, to improve estimation in small areas. Thus, for estimating poverty in small areas, the CPS can be used in conjunction with the American Community Survey and other sources. Multivariate multi-level models that allow for missing data will be developed that make use of the correlations in the different surveys to increase the precision of the small area estimates, and theoretical properties of the estimates will be derived. Modifications of the multivariate models will allow information from longitudinal surveys to be combined as well, and allow longitudinal analyses from a panel survey to be supplemented by information from related cross-sectional surveys. The investigator will also develop methods for outlier detection and robust estimation using the multiple data sources.Increasing amounts of information are available from surveys and other sources, and there is increasing demand from federal and local governments and from social scientists for estimates in small areas. The models developed in this research will combine information from different surveys and use longitudinal and spatial aspects of the surveys to improve the accuracy of small area estimates, with no additional data collection cost. The theoretical results derived in this research will also be useful for other areas of application such as genetics and quality improvement. This research is supported by the Methodology, Measurement, and Statistics Program and a consortium of federal statistical agencies under the Research on Survey and Statistical Methodology Funding Opportunity.