Developing Statistical Tools for Data integration and Data Fusion for Finite Population Inference

开发用于有限总体推理的数据集成和数据融合的统计工具

• 批准号: 2242820
• 负责人: Jae-Kwang Kim
• 金额: $ 37.5万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242820&HistoricalAwards=false
• 关键词: Developing; Statistical; Tools; Data; integration

This research project will develop statistical learning tools for data integration and data fusion. Given the proliferation of new data sources, researchers are increasingly utilizing convenient but often uncontrolled big data sources, web survey panels, and administrative data. Data integration is an emerging field of study that combines multiple data sources in a reliable way. However, statistical tools for data integration are limited. The results of this research will significantly impact the analysis of complex survey data with big data, as well as scientific conclusions drawn from multiple data sets. The investigator plans to actively collaborate with researchers at different statistical agencies, and applications will be conducted to demonstrate the value of the new methods in various settings. The results of this research will be disseminated via publications, presentations, short courses, webinars, and software. Graduate students will be involved in the conduct of the research.This research project will produce statistical and machine learning tools for data integration and fusion. Statistical agencies face increasing pressure to utilize convenient but often uncontrolled sources of data. While such data sources provide timely data for a large number of variables and population elements, they often fail to represent the target population of interest because of inherent selection biases. By using an independent probability sample as a calibration sample, the selection bias in the convenience sample can be reduced; however, the statistical tools for data integration are not yet satisfactory. In survey sampling research, statistical inference combining multiple data sources is a relatively understudied topic. This research will expand the scope of survey data analysis by providing numerous statistical and machine learning tools for data integration and by enhancing the use of data integration through example applications. The investigator will address important research topics such as mass imputation using modern machine learning tools, propensity score weighting using information projection, calibration weighting with high dimensional covariates, multiple bias calibration for data integration, and optimal estimation and sampling design for data fusion.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该研究项目将开发用于数据集成和数据融合的统计学习工具。鉴于新数据源的扩散，研究人员越来越多地利用方便但通常不受控制的大数据源，网络调查小组和管理数据。数据集成是一个新兴的研究领域，以可靠的方式结合了多个数据源。但是，数据集成的统计工具是有限的。这项研究的结果将显着影响使用大数据对复杂调查数据的分析，以及从多个数据集得出的科学结论。研究人员计划与不同统计机构的研究人员积极合作，并将进行应用程序以证明各种环境中新方法的价值。这项研究的结果将通过出版物，演示文稿，短期课程，网络研讨会和软件传播。研究生将参与研究的进行。此研究项目将生产用于数据集成和融合的统计和机器学习工具。统计机构面临着增加的压力，以利用方便但通常不受控制的数据来源。尽管此类数据源为大量变量和人口元素提供了及时的数据，但由于固有的选择偏见，它们通常无法代表目标人群。通过使用独立的概率样本作为校准样本，可以减少方便样本中的选择偏差。但是，数据集成的统计工具尚不令人满意。在调查抽样研究中，结合多个数据源的统计推断是一个相对研究的主题。这项研究将通过提供众多用于数据集成的统计和机器学习工具并通过示例应用程序增强数据集成的使用来扩大调查数据分析的范围。研究者将讨论重要的研究主题，例如使用现代机器学习工具进行大规模插补，使用信息投影，具有高维协变量的校准加权，校准加权，多个偏差校准数据集成以及数据融合的最佳估计和抽样设计，以反映NSF的法定任务和通过评估的范围来弥补，这表明了审查的范围，并具有范围的范围。