NCRN-MN: Triangle Census Research Network

许多联邦统计机构的一项主要任务是向公众发布数据以供二次分析。然而，由于意外的保密泄露、不回应和错误数据的风险，以及收集许多详细属性的不断增加的调查成本，传播越来越具有挑战性。三角普查研究网络（TCRN）将开发广泛适用的方法，以改变和改善联邦统计系统的数据传播实践。特别是，TCRN将通过发展理论和方法来发布基于灵活的、非参数贝叶斯模型（专门为纵向和多层次的高维数据构建）的多输入合成数据集，从而推进传播具有高质量和可接受的机密泄露风险的公共使用数据的方法和工具。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，而不会导致机密性泄露。该项目还将开发计算机系统框架，为二级分析人员提供关于编校数据推断质量的反馈，并将开发基于线性规划和贝叶斯建模融合的合成列联表的理论和方法。TCRN将改进处理缺失和错误数据的方法和实践，通过整合统计学和运筹学的范例，开发缺失数据同时输入和错误数据编辑的框架。该项目还将开发非参数贝叶斯方法，用于纵向和多层次的高维缺失数据的多次输入。最后，为了提高机构整合来自多个来源的信息的能力，TCRN将开发方法，使机构和二级分析师可以使用这些方法在不完善的记录链接设置中正确解释推断中的不确定性，并通过多个输入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，将来自不依赖于记录联系的多个数据源的信息结合起来。TCRN在方法上的发展将改变统计机构处理数据传播的方式，涉及统计披露的限制、数据缺失和信息整合。这些发展将为联邦机构提供更多的选择，以发布具有更高效用的数据产品，从而推动科学进步和改善政策制定。TCRN将把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次二次分析。TCRN的跨学科团队将使用这些数据产品来回答老龄化、经济和社会福利方面的问题，这些问题对政策制定具有重要意义。作为研究的一个组成部分，TCRN将参与并为联邦机构的博士后、研究生和统计学家提供教育机会，从而培养和培训数据传播研究和实践的未来领导者。这项活动是由nsf人口普查研究网络资助的机会。

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.