Collaborative Research: Bayesian and Likelihood Based Multilevel Models for Small Area Estimation

协作研究：用于小区域估计的基于贝叶斯和似然的多级模型

• 批准号: 0221857
• 负责人: Tapabrata Maiti
• 金额: $ 4.51万
• 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: 2002
• 资助国家: 美国
• 起止时间: 2002-01-01 至 2003-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0221857&HistoricalAwards=false
• 关键词: Collaborative; Research; Bayesian; Likelihood; Based

This research project focuses on Bayesian and likelihood based multilevel models for small area estimation. These methods will be compared and contrasted against some of the existing methods, such as the pseudo maximum likelihood, penalized quasilikelihood, etc. Some of the novel features of this research will be the use of stratum varying regression coefficients, new priors for the variance-covariance matrix rather than the standard Wishart prior, development of small area estimation models allowing measurement errors for covariates, use of hierarchical likelihood in the context of small area estimation, and the use of survey weights for small area estimation. One of the major applications of this project will be the estimation of income and poverty for states and counties, and possibly even for lower levels of geography such as census tracts and school districts (when data become available) between decennnial censuses. However, the methods are fairly general, and can be applied to other studies as well. Among others, these methods will be applied to study youth unemployment for small areas based on the Scottish School Leavears Survey, effectiveness of schools and student character in an education survey conducted by the Inner London Education Authority, and a British Social Attitudes Survey.The terms "small area'' or "local area" are commonly used to denote a small geographical area, such as a county, a municipality, or a census division. They may also describe a "small domain;" that is, a small subpopulation such as a specific age-sex-race group of people within a large geographical area. In these days, there is a global need for reliable small area statistics both from the private and public sectors. There are increasing government concerns with issues of distribution, equity, and disparity. For example, there may exist geographical subgroups within a given population that are handicapped in many respects, and need definite upgrading. Before taking remedial action, there is a need to identify such regions, and accordingly, one must have statistical data at the relevant geographical levels. Small area statistics also are needed in the apportionment of government funds, and in regional and city planning. In addition, there are demands from the private sector since the policy-making of many businesses and industries relies on local socio-economic conditions. Thus, small area estimation techniques have global applicability, and are useful for diverse applications.

该研究项目重点关注用于小区域估计的贝叶斯和基于可能性的多级模型。 这些方法将与一些现有的方法进行比较和对比，例如伪最大似然法、惩罚拟似然法等。这项研究的一些新颖特征将是使用层变化回归系数、方差的新先验。协方差矩阵而不是标准的 Wishart 先验，开发允许协变量测量误差的小区域估计模型，在小区域估计的背景下使用分层似然，以及使用调查权重进行小区域估计。 该项目的主要应用之一是估计各州和县的收入和贫困，甚至可能估计十年人口普查之间的较低水平的地理区域，例如人口普查区和学区（当数据可用时）。 然而，这些方法相当通用，也可以应用于其他研究。 除其他外，这些方法将用于根据苏格兰学校休假调查、内伦敦教育局进行的教育调查中的学校效率和学生性格以及英国社会态度调查来研究小地区的青年失业率。 “小区域”或“局部区域”通常用于表示较小的地理区域，例如县、市或人口普查部门。它们也可以描述“小域”；即，一个小的子群体，例如作为具体的如今，全球范围内都需要来自私营和公共部门的可靠的小区域统计数据。政府越来越关注分配、公平和不平等问题。例如，某一特定人群中可能存在在许多方面存在缺陷的地理亚群，需要进行明确的升级，在采取补救措施之前，需要确定这些区域，因此必须有相关的统计数据。地理层面。 政府资金分配、区域和城市规划也需要小区域统计。 此外，由于许多企业和行业的政策制定依赖于当地的社会经济条件，因此私营部门也有需求。 因此，小区域估计技术具有全球适用性，并且可用于多种应用。