Collaborative Research: Computation-driven small area inference with applications

协作研究：计算驱动的小区域推理与应用

• 批准号: 0851001
• 负责人: Parthasarathi Lahiri
• 金额: $ 9.75万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0851001&HistoricalAwards=false
• 关键词: Collaborative; Research; Computation; driven; small

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).There is a growing demand to assess various measures of human well-being (including socio-economic and health characteristics) at national and sub-national levels. However, data availability at the sub-national level is often limited owing to constraints such as costs of data collection, natural limitations, confidentiality and other ethical issues. This research aims to extract relevant information for predictive purposes from several data sources and provide sound predictions under limited-data conditions. Since small area statistics are routinely used by a diverse group of stakeholders, there is an urgent need to develop different reliable risk measures - this is the prime focus of this project. More specifically, a unified computational method will be developed to estimate different risk measures across a broad spectrum of statistical models, methods of estimation of model parameters, types of data, and symmetric or asymmetric loss functions. The proposed research will replace complicated analytical formulae, often hard or impossible to obtain, by simple efficient techniques that use the efficiencies of high power computing. Extensive evaluation of the proposed techniques will be carried out using computer-generated and complex survey datasets. This research will extend the scope of small area statistics to several applications of public interest and will strengthen related educational programs.

该奖项是根据2009年《美国复苏与再投资法》（公法111-5）资助的。在国家和次级国家层面评估人类福祉的各种措施（包括社会经济和健康特征）的需求日益增长。但是，由于限制了数据收集成本，自然限制，机密性和其他道德问题，因此在次国国家层面上的数据可用性通常受到限制。这项研究旨在从几个数据源中提取相关信息，以实现预测目的，并在有限的数据条件下提供合理的预测。由于小区域统计数据是由多样化的利益相关者常规使用的，因此迫切需要制定不同的可靠风险措施 - 这是该项目的主要重点。更具体地说，将开发一种统一的计算方法，以估计各种统计模型，模型参数估计方法，数据类型以及对称或不对称损失函数的不同风险度量。拟议的研究将通过使用高功率计算效率的简单有效技术来取代复杂的分析公式，通常很难或无法获得。将使用计算机生成且复杂的调查数据集对所提出的技术进行广泛的评估。这项研究将将小面积统计数据的范围扩展到公共利益的几种应用，并将加强相关的教育计划。