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: 9978145
• 负责人: Parthasarathi Lahiri
• 金额: $ 7.33万
• 依托单位: University of Nebraska-Lincoln
• 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 2002-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9978145&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.

大规模样本调查通常旨在对大地理区域的各种感兴趣特征产生可靠的估计。 但是，为了有效地计划健康，社会和其他服务，对较小的地理区域和亚群的类似估计的需求日益增加，通常称为小区域（或小域）。 当使用数据将政府资金分配到各个群体之间时，小区域统计数据的准确性尤为重要。本项目着重于开发新的健壮小区域估计方法和相关的模型诊断。 估计方法将在一般的多级模型下开发，这些模型将有助于解决各种小区域估计问题。 为了解决与多级模型相关的模型验证和模型选择的重要但很大程度上被忽略的方面，提出了使用样品分割技术的测试。 将样本分为估计集，并可以使用验证集来评估模型的实际功能。 随着社会科学家发现需要使用复杂的多层模型来解决他们的问题，这一研究领域将继续发展。研究是研究人员在各种联邦，州和私人机构遇到的小区域估计问题的经历中的产物。 重要的是，该项目将解决世界各地许多政府和私人机构工作的基础的关键实际问题。 此外，这项对小区域估计的研究还将显着促进有关调查抽样，广义线性混合模型，经验最佳预测理论，线性经验贝叶斯，方差成分估计，重新采样方法，模型诊断，更高级别的渐近级别和统计计算的文献。 由于不同类型的研究人员的利益（例如，调查抽样者，主要流统计学家，社会科学家），随着我们进入下一个千年，小区域的估计将仍然是调查采样中最有趣的问题之一。