"Nonparametric Estimation with Applications to Large and Complex Survey Data"

“非参数估计及其在大型和复杂调查数据中的应用”

• 批准号: 0905730
• 负责人: Lily Wang
• 金额: $ 10.02万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905730&HistoricalAwards=false
• 关键词: Nonparametric; Estimation; Applications; Large; Complex

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). One difficulty facing today's survey statisticians is the increasingly complex structures of surveys. The U.S. is very well provided with various sorts of longitudinal surveys which have considerable advantages over widely used cross-sectional data for capturing dynamic demographic relationships. It is desirable to make inferences from these complex surveys as model-free as they can be. Nonparametric statistics is a flexible and promising tool that properly reflects complex design structures. However, the simultaneous consideration of detection of survey errors with high dimensionality, smoothing and the additional complexity emerging from complex correlation structures presents great challenges in nonparametric survey analysis. The investigator works on novel nonparametric model-assisted methods for large and complex surveys, including longitudinal surveys, via incorporation of "cheap" auxiliary information. The current project includes (1) developing finer and more intelligent nonparametric tools for survey sampling; (2) investigating nonparametric survey methodology in the presence of nonsampling errors, such as nonresponse and measurement errors;(3) exploring new procedures and novel theory in longitudinal survey analysis.The field of survey research is undergoing profound and rapid changes brought on by larger societal, technological, and theoretical developments. With large complex surveys in many research areas becoming increasingly available for public use, the theory and practice in this proposal can serve as an important tool for survey practitioners, (bio)statisticians, epidemiologists, economists, sociologists, and other researchers.The proposed methodologies will significantly enrich the techniques of longitudinal survey modeling and broaden the traditional understanding of survey sampling. The proposed research will also strengthen the U.S. federal statistical system by providing survey researchers from several federal agencies (including Census Bureau, the National Center for Health Statistics, the Bureau of Justice Statistics, and the Bureau of Labor Statistics) modern and advanced methods in survey methodology.

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。当今调查统计人员面临的一个困难是调查的结构日益复杂。美国有各种各样的纵向调查，这些调查在捕捉动态人口关系方面比广泛使用的横截面数据具有相当大的优势。从这些复杂的调查中尽可能不使用模型来进行推断是可取的。非参数统计是一种灵活且有前途的工具，可以正确反映复杂的设计结构。然而，在非参数调查分析中，同时考虑高维调查误差的检测，平滑和复杂相关结构带来的额外复杂性提出了很大的挑战。该研究员致力于通过纳入“廉价”辅助信息，为大型和复杂的调查（包括纵向调查）开发新的非参数模型辅助方法。目前的项目包括：（1）开发更精细和更智能的非参数调查抽样工具;（2）研究存在非抽样误差（如无回答和测量误差）的非参数调查方法;（3）探索纵向调查分析的新方法和新理论。调查研究领域正在经历更大的社会，技术，和理论发展。随着许多研究领域的大型复杂调查越来越多地为公众所使用，该建议中的理论和实践可以作为调查从业者，（生物）统计学家，流行病学家，经济学家，社会学家和其他研究人员的重要工具，所提出的方法将显着丰富纵向调查建模技术，并拓宽传统的调查抽样的理解。拟议中的研究还将通过为来自几个联邦机构（包括人口普查局、国家卫生统计中心、司法统计局和劳工统计局）的调查研究人员提供现代和先进的调查方法，加强美国联邦统计系统。