Bayesian Methods for Small Area Estimation and Latent Structure Models

小区域估计和潜在结构模型的贝叶斯方法

• 批准号: 9423996
• 负责人: Malay Ghosh
• 金额: $ 17.19万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-05-01 至 1999-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9423996&HistoricalAwards=false
• 关键词: Bayesian; Methods; Small; Area; Estimation

The primary focus on this research is on applications of Bayesian methods to small area estimation with discrete outcomes. Small area estimation is becoming increasingly popular in survey sampling owing to the demand for small area estimates from both public and private sectors. In typical instances of small area estimation, only a few samples are available from individual areas. The direct survey estimates, therefore, tend to have large standard errors and coefficients of variation. Incorporating information from similar neighboring areas typically improves an estimate of a certain area mean, or the simultaneous estimation of several area means. Empirical and hierarchical Bayes methods are particularly well-suited to meet this need of `borrowing strength` from related small areas. The new methods of estimation will provide much more reliable estimates with reduced standard errors. The investigator will apply the new methods to the analysis of various social, medical, and environmental data; e.g., the investigator will estimate the percentage of people satisfied with their job in several local areas cross-classified by age, sex, and race. Other possible applications include exposure to health hazards in jobs, estimation of cancer mortality rates, analysis of mortality rates in the presence of hazardous waste sites, and analysis of spatial data. Another aspect of the investigator's research will concentrate on hierarchical and empirical Bayes analysis of latent structure models. The celebrated Rasch model will be included as a special case. This analysis will provide unified method for the analysis of social and psychological data.

本研究的主要重点是贝叶斯方法在离散结果小面积估计中的应用。由于公共和私营部门对小面积估计的需求，小面积估计在调查抽样中越来越受欢迎。在小面积估计的典型情况下，只有少数样本来自个别地区。因此，直接调查估计往往有较大的标准误差和变异系数。结合来自相似相邻区域的信息通常可以提高对某个区域均值的估计，或者同时对几个区域均值的估计。经验和层次贝叶斯方法特别适合于满足这种从相关小领域“借用力量”的需求。新的估计方法将提供更可靠的估计和减少标准误差。研究者将运用新方法分析各种社会、医疗和环境数据；例如，调查人员将根据年龄、性别和种族的交叉分类，估计几个地方对工作满意的人的百分比。其他可能的应用包括在工作中接触健康危害、估计癌症死亡率、分析存在危险废物场址的死亡率以及分析空间数据。研究者研究的另一个方面将集中在潜在结构模型的层次和经验贝叶斯分析。著名的拉希模型将作为一个特例被包括在内。这一分析将为社会和心理数据的分析提供统一的方法。