Collaborative Research: Computation-driven small area inference with applications

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。在国家和国家以下各级评估人类福祉（包括社会经济和健康特征）的各种措施的需求日益增长。 然而，由于数据收集费用、自然限制、保密和其他道德问题等制约因素，国家以下一级的数据提供往往有限。 本研究旨在从多个数据源中提取用于预测目的的相关信息，并在有限数据条件下提供合理的预测。 由于小地区统计数据经常被各种利益攸关方使用，因此迫切需要制定不同的可靠风险措施？这是这个项目的主要重点。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非对称损失函数的不同风险度量。拟议的研究将取代复杂的分析公式，往往很难或不可能获得，通过简单有效的技术，利用高功率计算的效率。将利用计算机生成的复杂调查数据集对拟议技术进行广泛评价。这项研究将扩大小区域统计的范围，使其应用于公共利益，并将加强相关的教育计划

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