Advances for Complex Surveys with Auxiliary Information and Missing Data

利用辅助信息和缺失数据进行复杂调查的进展

• 批准号: 1155668
• 负责人: Zhiqiang Tan
• 金额: $ 5万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1155668&HistoricalAwards=false
• 关键词: Advances; Complex; Surveys; Auxiliary; Information

The objective of this research is to develop a new conceptual framework together with an effective analytical approach for survey sampling. The new framework centers on explicitly linking survey sampling to missing data problems. Conceptually, inference from a survey sample is similar to that in a missing data problem: study variables are observed for subjects in the sample, but are missing for those outside the sample. This researcher will develop efficient estimation, by first constructing efficient estimators, for example, based on nonparametric maximum likelihood for missing data problems with independent and identically distributed data and then extending those estimators to survey sampling.Survey sampling is widely used for information gathering and analysis in various settings, including government agencies, academic institutions, and industries. This research will help to draw more accurate inferences than before from survey data. This will improve the cost-effectiveness of surveys and lead to better informed policy analysis and scientific investigation. Computer programs for implementing the methods will be made publicly available.

本研究的目的是开发一个新的概念框架以及有效的调查抽样分析方法。新框架的核心是明确地将调查抽样与缺失数据问题联系起来。从概念上讲，从调查样本中得出的推论类似于在数据缺失问题中得出的推论：在样本中可以观察到研究变量，但在样本之外则无法观察到研究变量。本研究将开发有效的估计，首先构建有效的估计，例如，基于非参数极大似然的缺失数据问题，具有独立和同分布的数据，然后将这些估计扩展到调查抽样。调查抽样广泛用于各种环境中的信息收集和分析，包括政府机构、学术机构和行业。这项研究将有助于从调查数据中得出比以前更准确的推论。这将提高调查的成本效益，并导致更明智的政策分析和科学调查。实施这些方法的计算机程序将向公众开放。