Collaborative Research: Best Predictive Small Area Estimation

协作研究：最佳预测小区域估计

• 批准号: 1122399
• 负责人: Jonnagadda Rao
• 金额: $ 7.86万
• 依托单位: University of Miami School of Medicine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1122399&HistoricalAwards=false
• 关键词: Collaborative; Research; Best; Predictive; Small

Surveys usually are designed to produce reliable estimates of various characteristics of interest for large geographic areas or socio-economic domains. However, for effective planning of health, social, and other services and for apportioning government funds, there has been a growing demand to produce similar estimates for small geographic areas and subpopulations, commonly referred to as small areas. This research project aims at developing a new method of small area estimation that potentially will lead to a dramatic improvement in accuracy over the traditional methods in practical situations. Model-based small area estimation utilizes statistical models, such as mixed effects models, to "borrow strength." In particular, the empirical best linear unbiased prediction (EBLUP) is a well-known model-based method that has had dominant influence in small area estimation. From a practical point of view, however, any proposed model is subject to model misspecification. When the proposed statistical model is incorrect, EBLUP is no longer efficient or even effective. In such cases, a new method, known as observed best prediction (OBP), may be superior. This project involves several important research topics on OBP, including theoretical developments, assessment of uncertainties under weak model assumptions, and implementation of the OBP via user-friendly software. The research largely will expand the results of our earlier studies, and contribute to making the OBP method more effective, practical, and easy to use.The research introduces a completely new idea and method to model-based statistical methods in survey sampling. It is expected to impact other scientific areas where statistical methods have been used for prediction problems. The project will develop and freely disseminate R code to implement the OBP method. The education component of the project will introduce the OBP method into courses at the investigators' institutes. These courses are expected to draw students and researchers from statistics, biostatistics, genetic epidemiology, animal and plant sciences, educational research, social sciences, and government agencies. The project is supported by the Methodology, Measurement, and Statistics Program and a consortium of federal statistical agencies as part of a joint activity to support research on survey and statistical methodology.

调查的目的通常是对大片地理区域或社会经济领域感兴趣的各种特征作出可靠的估计。然而，为了有效规划卫生、社会和其他服务，以及分配政府资金，越来越多的人要求对小地理区域和亚群人口(通常称为小区域)进行类似的估计。该研究项目旨在开发一种新的小区域估计方法，该方法在实际应用中可能会比传统方法的精度有很大的提高。基于模型的小区域估计利用统计模型，如混合效应模型，来“借力”。特别是，经验最佳线性无偏预测(EBLUP)是一种众所周知的基于模型的方法，在小区域估计中具有主导影响。然而，从实践的角度来看，任何拟议的模型都会受到模型错误说明的影响。当所提出的统计模型不正确时，EBLUP不再有效，甚至不再有效。在这种情况下，一种被称为观测最佳预测(OBP)的新方法可能会更好。该项目涉及OBP的几个重要研究课题，包括理论发展、弱模型假设下的不确定性评估以及通过用户友好的软件实现OBP。本研究将在很大程度上拓展以往的研究成果，使OBP方法更有效、更实用、更易于使用。本研究为基于模型的调查抽样统计方法引入了一种全新的思路和方法。预计它将影响其他科学领域，这些领域已经使用统计方法来解决预测问题。该项目将开发和免费传播实施OBP方法的R代码。该项目的教育部分将把OBP方法引入调查机构的课程。这些课程预计将吸引来自统计学、生物统计学、遗传流行病学、动植物科学、教育研究、社会科学和政府机构的学生和研究人员。作为支持调查和统计方法研究的联合活动的一部分，该项目得到了方法学、测量和统计方案和一个联邦统计机构联盟的支持。