Collaborative Research: Small-Area Estimation - A Growing Problem for the Next Millennium

协作研究：小区域估计 - 下一个千年日益严重的问题

• 批准号: 9978101
• 负责人: Jiming Jiang
• 金额: $ 5.41万
• 依托单位: Case Western Reserve University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-15 至 2001-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9978101&HistoricalAwards=false
• 关键词: Collaborative; Research; Small; Area; Estimation

Large scale sample surveys are usually designed to produce reliable estimates of various characteristics of interest for large geographic areas. However, for effective planning of health, social, and other services, there is a growing demand to produce similar estimates for smaller geographic areas and subpopulations, commonly referred to as small-areas (or small-domains). The accuracy of small-area statistics is especially crucial when data are used to apportion government funds among various groups.This project focuses on development of new robust small-area estimation methods and the associated model diagnostics. The estimation methods will be developed under general multi-level models which will be useful in solving a variety of small-area estimation problems. To address an important and yet largely neglected aspect of model validation and model selection associated with multi-level models, a test using a sample splitting technique is proposed. Splitting the sample into an estimation set and a validation set can also be used for assessing the actual power of the model. This area of research will continue to grow as social scientists find the need to use complex multi-level models to solve their problems.The research is an outgrowth of the investigators' experiences with small-area estimation problems encountered by various federal, state, and private agencies. Importantly, this project will address a crucial practical problem underlying the work of many governmental and private institutions throughout the world. Further, this research on small-area estimation also will contribute significantly to the literature on survey sampling, generalized linear mixed models, empirical best prediction theory, linear empirical Bayes, variance component estimation, resampling methods, model diagnostics, higher order asymptotics, and statistical computing. Because of the interests of different types of researchers (e.g., survey samplers, main stream statisticians, social scientists), small-area estimation will remain one of the most intriguing problems in survey sampling as we advance into the next millennium.

大规模抽样调查通常旨在对大地理区域的各种感兴趣的特征进行可靠的估计。 然而，为了有效规划卫生、社会和其他服务，越来越多的人需要为较小的地理区域和子人群（通常称为小区域（或小域））提供类似的估计。 在使用数据向不同群体分配政府资金时，小面积统计的准确性尤为重要。本项目的重点是开发新的可靠的小面积估计方法和相关的模型诊断。 估计方法将在一般多层次模型下开发，这将有助于解决各种小面积估计问题。 为了解决一个重要的，但在很大程度上被忽视的方面与多层次模型的模型验证和模型选择，提出了一个测试使用样本分裂技术。 将样本分为估计集和验证集也可用于评估模型的实际功效。 随着社会科学家发现需要使用复杂的多层次模型来解决他们的问题，这一研究领域将继续发展。这项研究是研究人员在处理各种联邦、州和私人机构遇到的小区域估计问题方面的经验的产物。 重要的是，该项目将解决世界各地许多政府和私营机构工作中的一个关键实际问题。 此外，这项研究的小区域估计也将有助于显着的文献调查抽样，广义线性混合模型，经验最佳预测理论，线性经验贝叶斯，方差分量估计，恢复方法，模型诊断，高阶渐近和统计计算。 由于不同类型研究人员的兴趣（例如，调查抽样员、主流统计学家、社会科学家），小面积估计仍将是我们进入下一个千年时调查抽样中最令人感兴趣的问题之一。