NCRN-MN: Triangle Census Research Network

NCRN-MN：三角人口普查研究网络

• 批准号: 1131897
• 负责人: Jerome Reiter
• 金额: $ 299.76万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1131897&HistoricalAwards=false
• 关键词: NCRN; MN; Triangle; Census; Research

A primary mission of many federal statistical agencies is to disseminate data to the public for secondary analysis. However, dissemination is increasingly challenging due to risks of unintended confidentiality breaches, nonresponse and faulty data, and the costs of mounting surveys that collect many detailed attributes. The Triangle Census Research Network (TCRN) will develop broadly applicable methodologies that will transform and improve data dissemination practice in the federal statistical system. In particular, the TCRN will advance methodologies and tools for disseminating public use data with high quality and acceptable risks of confidentiality breaches by developing theory and methodology for releasing multiply imputed, synthetic datasets based on flexible, nonparametric Bayesian models built specifically for high-dimensional data with longitudinal and multi-level aspects. TCRN will develop approaches for including survey weights in redacted data that can improve statistical estimation without leading to confidentiality disclosures. The project also will develop the framework for computer systems that provide secondary analysts with feedback on the quality of inferences from redacted data, and it will develop theory and methodology for creating synthetic contingency tables based on fusions of linear programming and Bayesian modeling. The TCRN will improve methodology and practice for handling missing and faulty data by developing frameworks for simultaneous imputation of missing data and editing of faulty data by integrating paradigms from statistics and operations research. The project also will develop nonparametric Bayesian methodology for multiple imputation of missing data in high dimensions with longitudinal and multi-level aspects. Finally, to enhance agencies' abilities to integrate information from multiple sources, the TCRN will develop methods that agencies and secondary analysts can use to properly account for uncertainty in inferences in imperfect record linkage settings, as well as to pass on that uncertainty in public use data products via multiply imputed datasets. TCRN also will develop statistical approaches to combining information from multiple data sources that do not depend on record linkage.The methodological developments of the TCRN will transform the way statistical agencies handle data dissemination with regard to statistical disclosure limitation, missing data, and integrating information. These developments will offer federal agencies options for releasing data products with increased utility, leading to advances in science and improved policy making. The TCRN will apply the methodologies to major Census Bureau data products, thereby improving the hundreds of secondary analyses of these datasets. The interdisciplinary team of the TCRN will use these data products to answer questions in aging, economics, and social welfare that have important implications for policy making. As an integral part of the research, the TCRN will involve and offer educational opportunities to postdoctoral fellows, graduate students, and statisticians at federal agencies, thus developing and training future leaders in data dissemination research and practice. This activity is supported by the NSF-Census Research Network funding opportunity.

许多联邦统计机构的主要任务是向公众传播数据以供二次分析。 然而，由于意外泄露机密、不回复和错误数据的风险，以及收集许多详细属性的调查的成本，传播越来越具有挑战性。 三角人口普查研究网络 (TCRN) 将开发广泛适用的方法，以改变和改进联邦统计系统中的数据传播实践。 特别是，TCRN 将开发用于发布基于灵活、非参数贝叶斯模型的多重估算合成数据集的理论和方法，以推进传播高质量和可接受的保密泄露风险的公共使用数据的方法和工具，这些模型专门为具有纵向和多层次方面的高维数据而构建。 TCRN 将开发将调查权重纳入编辑数据的方法，以改进统计估计，而不会导致机密性披露。 该项目还将开发计算机系统框架，为二级分析师提供有关编辑数据推论质量的反馈，并将开发基于线性规划和贝叶斯建模融合创建综合列联表的理论和方法。 TCRN 将通过整合统计学和运筹学的范式，开发同时估算缺失数据和编辑错误数据的框架，改进处理缺失和错误数据的方法和实践。 该项目还将开发非参数贝叶斯方法，用于纵向和多层次高维缺失数据的多重插补。 最后，为了增强机构整合多个来源信息的能力，TCRN 将开发机构和二级分析师可以使用的方法，以正确解释不完善记录链接设置中推论的不确定性，并通过多重插补数据集传递公共使用数据产品中的不确定性。 TCRN 还将开发统计方法来组合来自不依赖于记录链接的多个数据源的信息。TCRN 方法的发展将改变统计机构在统计披露限制、缺失数据和整合信息方面处理数据传播的方式。 这些发展将为联邦机构提供发布实用性更高的数据产品的选择，从而推动科学进步和改进政策制定。 TCRN 会将这些方法应用于人口普查局的主要数据产品，从而改进对这些数据集的数百项二次分析。 TCRN 的跨学科团队将利用这些数据产品来回答对政策制定具有重要影响的老龄化、经济和社会福利问题。 作为研究的一个组成部分，TCRN 将让联邦机构的博士后、研究生和统计学家参与并为其提供教育机会，从而培养和培训数据传播研究和实践的未来领导者。 这项活动得到了 NSF-人口普查研究网络资助机会的支持。