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

基本信息

批准号：
8912527
负责人：
John R Logan
金额：
$ 19.8万
依托单位：
BROWN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-08-14 至 2018-02-28
项目状态：
已结题

项目摘要

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口样本的数据来描述小区域的特征，但是虽然已知基于随机样本的平均值可以提供无偏估计，但当样本很小。这个问题在使用已发布的人口普查数据（例如人口普查区）以及许多大规模的健康相关调查时出现。该项目评估贝叶斯模型在小区域估计中的性能，特别关注其在人口普查提供的人口数据类型中的性能。该项目利用了新来源的可用性：来自 1880 年和 1940 年人口普查的 100% 人口数据，可以对人们在城市中的位置进行地理编码。这允许从总体中随意抽取多个样本，并将这些样本的小区域估计值与实际总体值进行比较。同样的方法可以应用于低密度郊区和非中心城市县的 ED 的非地理编码数据。该方法在其他三个主要方面也具有创新性。首先，大多数 SAE 模型性能评估都集中在面积均值和比例估计值的偏差和方差上。该项目涉及对空间人口统计学家来说非常重要的另一个问题：跨地区的差异（用于评估空间不平等和居住隔离）。其次，认识到包括人口普查在内的大多数当代大规模调查都使用样本权重来表示复杂的调查设计并纠正单位无答复的模式，我们专门评估了将样本权重纳入分层贝叶斯框架的方法。关于单元级空间贝叶斯模型中样本权重的使用知之甚少。该项目将研究并进一步开发空间模型的新扩展来处理样本权重。第三，芝加哥 1940 年的估计将利用 1930 年普查区一级的数据，利用时间和空间的依赖性。在比较不同模型的结果时，我们将检查一系列情景、不同的样本比例和人口规模以及小区域的地理规模，估计平均值和比例，比较不同样本设计的效果，并评估利用多个指标的贡献。这项工作的结果将包括为大多数研究人员提供指导在不同的设计场景下使用适当的统计方法。