"Nonparametric Estimation with Applications to Large and Complex Survey Data"
"Nonparametric Estimation with Applications to Large and Complex Survey Data"
批准号:
0905730
负责人:
Lily Wang
金额:
$10.02万
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
项目状态:
已结题
起止时间:
2009-07-01 至 2013-06-30
中文摘要
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英文摘要
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.
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会议论文
Conference: Track 1: The 2022 Big Ten Womens Workshop
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批准号:2227147
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项目类别:Standard Grant
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资助金额:$4.74万
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财政年份:2022
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负责人:Lily Wang
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依托单位:
Statistical Modelling and Inference for Next-Generation Functional Data
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批准号:2203207
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项目类别:Standard Grant
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资助金额:$12.5万
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财政年份:2021
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负责人:Lily Wang
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依托单位:
Statistical Modelling and Inference for Next-Generation Functional Data
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批准号:1916204
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项目类别:Standard Grant
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资助金额:$12.5万
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财政年份:2019
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负责人:Lily Wang
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依托单位:
Statistical Inference for Functional Data in Time Series and Survey Sampling: Theory and Methods
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批准号:1542332
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项目类别:Standard Grant
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资助金额:$10.0万
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财政年份:2014
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负责人:Lily Wang
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依托单位:
Statistical Inference for Functional Data in Time Series and Survey Sampling: Theory and Methods
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批准号:1309800
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项目类别:Standard Grant
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资助金额:$10.0万
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财政年份:2013
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负责人:Lily Wang
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依托单位:
CAREER: Integrating Time-Variant Source Directivity into Architectural Acoustic Auralizations
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批准号:0134591
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项目类别:Standard Grant
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资助金额:$37.74万
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财政年份:2002
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负责人:Lily Wang
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依托单位:
海外基金