Collaborative Research: Best Predictive Small Area Estimation

协作研究：最佳预测小区域估计

• 批准号: 1121794
• 负责人: Jiming Jiang
• 金额: $ 6.95万
• 依托单位: University of California-Davis
• 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 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1121794&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地理区域或社会经济领域的各种重要特征作出可靠的估计。但是，为了有效规划保健、社会和其他服务以及分配政府资金，越来越需要对小地理区域和亚人口（通常称为小地区）作出类似的估计。本研究项目旨在开发一种新的小面积估计方法，在实际情况下，这种方法可能会大大提高传统方法的精度。基于模型的小面积估计利用统计模型，如混合效应模型，来“借用力量”。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，在小面积估计中占有主导地位。然而，从实际的角度来看，任何被提议的模型都会受到模型错误说明的影响。当提出的统计模型不正确时，EBLUP就不再有效。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。本项目涉及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软件实现OBP。本研究将在很大程度上扩展我们之前的研究成果，并有助于使OBP方法更加有效、实用和易于使用。该研究为基于模型的调查抽样统计方法提供了一种全新的思路和方法。预计它将影响其他科学领域，在这些领域，统计方法已被用于预测问题。该项目将开发并自由传播R代码来实现OBP方法。该项目的教育部分将在研究机构的课程中引入OBP方法。这些课程将吸引来自统计学、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。该项目由方法、测量和统计项目和联邦统计机构联盟支持，作为支持调查和统计方法研究的联合活动的一部分。