Bayesian Methods and Inference

贝叶斯方法和推理

• 批准号: 9201210
• 负责人: Malay Ghosh
• 金额: $ 15万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-07-15 至 1996-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9201210&HistoricalAwards=false
• 关键词: Bayesian; Methods; Inference

This research concentrates on several theoretical investigations and applications of Bayesian methods to small area estimation, as well as the study of Bayesian robustness in finite population sampling , and Bayesian information. Recently the use of small area estimation has been increasing in survey sampling. Agencies of the Federal Government have been involved in obtaining estimates of unemployment rates, per capita income, crop yields, and so forth, for state and local government areas. In typical instances, only a few samples are available from an individual area, and an estimate of a certain area mean, or simultaneous estimates of several area means, can be improved by incorporating information from similar neighboring areas. Empirical and hierarchical Bayes methods are particularly well-suited to meet this need of "borrowing strength" from related small areas. The principal investigator, over the years, has developed general univariate Bayesian methods with applications to small area estimation. The present research is concerned with the extension of such methods to multivariate small area estimation problems and with the analysis of repeated survey data, which amounts to time series analysis. Specific applications will be made to the study of undercount adjustment in the 1990 census, estimation of median incomes of three-, four-, and five-person families as needed by the Department of Health and Human Services, and the estimation of all employee totals in non-agricultural industrial establishments as needed by the Bureau of Labor Statistics. The principal theoretical investigation will consist of the robustness study of Bayesian procedures, that is, how robust a Bayesian procedure is against departures from the assumed prior. Specific applications of these robustness ideas will be made in the context of finite population sampling. The final theoretical investigation will consist of an analytical study of the information content in different priors through a pure posterior analysis. Profession Ghosh is well equipped to perform the proposed research. His past work in this area has had substantial impacts and the present quartet of projects should yield both important methodological and applied results.

本研究集中在几个理论 小区域贝叶斯方法的研究与应用 估计，以及研究贝叶斯鲁棒性在有限 人口抽样和贝叶斯信息。 近年来，小面积估计的使用越来越多 在抽样调查中。 联邦政府各机构已 参与获得人均失业率估计数的人员 收入，作物产量，等等，为国家和地方 政府领域。 在典型的情况下，只有少数样品是 一个地区的可用资源， 面积平均值或多个面积平均值的同时估计值可以 通过合并来自相似邻居的信息来改进 地区 经验和层次贝叶斯方法特别适用于 很好地适应了这种向有关方面“借力”的需要， 小区域。 多年来，首席研究员， 具有 开发了一般单变量贝叶斯方法，并应用于 小面积估计 本研究关注的是 将这种方法推广到多变量小区域估计 问题和分析重复调查数据， 时间序列分析。 具体应用将 对1990年人口普查中的少计调整研究所作的研究， 估计三人、四人和五人的收入中位数 卫生和公共服务部需要的家庭， 以及非农业部门所有雇员总数的估计 劳动局需要的工业企业 统计 主要的理论研究将包括 贝叶斯程序的鲁棒性研究，也就是说， 贝叶斯程序是反对偏离假定的先验。 这些鲁棒性思想的具体应用将在 有限人口抽样的背景。 最后的理论 调查将包括一项分析研究， 通过纯后验检验不同先验的信息量 分析. Ghosh专业人员有能力执行拟议的 research. 他过去在这一领域的工作产生了重大影响 目前的四个项目应该产生重要的 方法和应用结果。