Investigating and Extending Bayesian Methods for Small Area Estimation

研究和扩展小区域估计的贝叶斯方法

• 批准号: 8769041
• 负责人: John R Logan
• 金额: $ 24.38万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-14 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8769041
• 关键词: Address; Affect; Age; American; Area; Bayesian Method; Bayesian Modeling; Behavioral Risk Factor Surveillance System; Censuses; Characteristics; Chicago; Cities; Code; Community Surveys; Complex; County; Data; Data Set; Demographer; Dependence; Effectiveness; Ethnic Origin; Family; Geographic Locations; Geography; Goals; Guidelines; Health; Inequality; Literature; Location; Methods; Modeling; Neighborhoods; Pattern; Performance; Population; Population Group; Population Sizes; Positioning Attribute; Public Health; Publishing; Race; Research Personnel; Residential Mobility; Risk; Sample Size; Sampling; Simulate; Social Class; Solutions; Source; Statistical Methods; Subgroup; Surveys; Techniques; Time; Variant; Weight; Work; base; compare effectiveness; cost; density; design; improved; innovation; novel; public health relevance; segregation; suburb

DESCRIPTION (provided by applicant): Researchers often wish to characterize small areas based on data from a sample of the local population, but while average values based on random samples are known to provide unbiased estimates, the variance of estimates can be quite large when samples are small. This problem arises in the use of published census data (such as for census tracts) as well as for many large-scale health-related surveys. This project evaluates the performance of Bayesian models for small area estimation with a particular focus on their performance with population data of the type provided by the census. The project exploits the availability of a new source: 100% population data from the Censuses of 1880 and 1940 where people's locations within cities can be geocoded. This allows multiple samples to be drawn at will from the population, and for small area estimates from these samples to be compared with the actual population values. The same methods can be applied with non-geocoded data for EDs in low density suburbs and for non-central city counties. The approach is innovative in three other major ways. First, most assessments of SAE model performance focus on the bias and variance in estimates of area means and proportions. The project deals with another concern that is substantively important to spatial demographers: the variance across areas (which is used to assess spatial inequality and residential segregation). Second, recognizing that most contemporary large scale surveys including the census use sample weights to represent complex survey designs and correct for patterns of unit non-response, we specifically assess methods to incorporate sample weights in the hierarchical Bayesian framework. Little is known about use of sample weights in unit-level spatial Bayesian models. This project will examine and further develop a new extension of spatial models to deal with sample weights. Third, the 1940 estimates for Chicago will draw on both dependence in both time and space, using data at the Enumeration District level in 1930. In comparing results from different models we will examine a range of scenarios, varying sample proportions and the population size and geographic scale of small areas, estimating both means and proportions, comparing the effects of different sample designs, and assessing the contribution of drawing on multiple indicators. The result of this work will include guidelines for investigators for the most appropriate statistical method to use under the different design scenarios.

描述（由申请人提供）：研究人员通常希望根据当地人群样本的数据来表征小面积，但是虽然已知基于随机样本的平均值可提供无偏见的估计，但是当样本较小时，估计值的差异可能很大。此问题出现在使用已发表的人口普查数据（例如普查区）以及许多与健康相关的大规模调查时出现的。该项目评估了贝叶斯模型进行小面积估计的性能，并特别关注他们的人口普查类型的人口数据。该项目利用了一个新来源的可用性：从1880年和1940年的人口普查中的100％人口数据，可以将城市内的人们的位置地理编码。这允许从人口随意绘制多个样本，对于这些样本的小面积估计，可以将其与实际人口值进行比较。可以将同样的方法与低密度郊区和非中央城市县的EDS进行非斜编码数据一起应用。这种方法在其他三个主要方面具有创新性。首先，大多数对SAE模型性能的评估都集中在面积平均值和比例估计值的偏差和差异上。该项目涉及另一个对空间人口统计学者至关重要的问题：跨领域的差异（用于评估空间不平等和住宅隔离）。其次，认识到大多数当代的大规模调查，包括普查使用样本权重代表复杂的调查设计并纠正单位无响应的模式，我们专门评估了将样品权重纳入层次贝叶斯框架中的方法。关于单位级空间贝叶斯模型中样品权重的使用知之甚少。该项目将检查并进一步开发空间模型的新扩展，以处理样本权重。第三，1940年芝加哥的估计将利用1930年枚举区域的数据来借鉴时间和空间的依赖。在比较不同模型的结果时，我们将研究一系列场景，样本比例以及小面积的种群规模和小面积的种群规模和地理规模，从而估算了不同的样品设计的效果，并评估了多个指示器的贡献，并估算了多次指示器的效果。这项工作的结果将包括针对研究人员的指南 在不同的设计方案下使用的适当统计方法。