Bayesian Methodology for Disclosure Limitation and Statistical Analysis of Large Government Surveys

大型政府调查的披露限制和统计分析的贝叶斯方法

• 批准号: 0106914
• 负责人: Roderick J.A. Little
• 金额: $ 35.53万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-10-01 至 2005-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0106914&HistoricalAwards=false
• 关键词: Bayesian; Methodology; Disclosure; Limitation; Statistical

Surveys with complex probability sampling designs involving clustering and stratification are a major source of empirical data for governmental and scientific use. The standard approach to survey inference is design-based, with statistical inferences being based on the sampling distribution with population values treated as fixed. This approach has been a powerful force for the development of objective statistical analysis of large surveys, with reliable operating characteristics under weak assumptions about the population. However, the design-based paradigm is too limited to handle: (i) the increased availability of data from a variety of sources, such as surveys, censuses and administrative records; (ii) increasing demands for analyses that go beyond simple descriptive information such as means and totals for large domains; (iii) the development and analysis of data masked to preserve confidentiality; and (iv) the analysis of data subject to unit and item nonresponse. These questions can be addressed by a model-based Bayesian approach, with models that capture the relevant features of the population under study and take into account important features of the sample design, and non-informative priors that limit subjectivity in the analysis.Bayesian methods are enjoying a resurgence in statistics, with the development of computational tools that make them practically feasible. However, the application of Bayesian methods to sample surveys remains very limited. The goal of this research is to develop useful, practical Bayesian methods for sample survey inference that have good design-based properties. The dissemination of public use data files is crucial to the research community in order to conduct research that forms the basis for rational policy decisions. This research will develop methods for disseminating detailed micro-data files that greatly reduce the risk of disclosure of the identity of respondents to a data intruder. Methods will be based on multiple imputation of key variables, an approach that allows for valid statistical inferences using existing software and limits the degree of information loss. The methods will be tested on large government surveys collected by the National Center for Health Statistics and other federal agencies. This research also will develop Bayesian methods for three topics in the statistical analysis of complex surveys: (i) the analysis of surveys where the sampled units have differential probabilities of inclusion; (ii) the handling of unit and item nonresponse in surveys; and (iii) the analysis of samples collected using rotating panel designs. This research is supported by the Methodology, Measurement, and Statistics Program and a consortium of federal statistical agencies under the Research on Survey and Statistical Methodology Funding Opportunity.

涉及聚类和分层的复杂概率抽样设计的调查是政府和科学使用的经验数据的主要来源。 调查推断的标准方法是以设计为基础的，统计推断是以抽样分布为基础的，人口值被视为固定的。 这一方法是对大型调查进行客观统计分析的强大力量，在对人口的弱假设下具有可靠的操作特性。 然而，基于设计的模式太有限，无法处理：㈠来自各种来源的数据越来越多，如调查、人口普查和行政记录; ㈡对分析的需求越来越大，超出了简单的描述性信息，如大领域的手段和总数; ㈢为保密而掩盖数据的开发和分析;以及（iv）对受单元和项目无应答影响的数据的分析。 这些问题可以通过基于模型的贝叶斯方法来解决，该方法的模型捕捉了所研究人群的相关特征，并考虑了样本设计的重要特征，以及限制分析中主观性的非信息先验。 然而，贝叶斯方法在抽样调查中的应用仍然非常有限。 本研究的目标是开发有用的，实用的贝叶斯抽样调查推理方法，具有良好的设计为基础的属性。 传播公共使用数据文件对研究界至关重要，以便开展研究，为合理的政策决定奠定基础。 这项研究将制定传播详细的微观数据文件的方法，大大减少向数据入侵者披露答复者身份的风险。 方法将基于关键变量的多重插补，这种方法允许使用现有软件进行有效的统计推断，并限制信息损失的程度。 这些方法将在国家卫生统计中心和其他联邦机构收集的大型政府调查中进行测试。 本研究还将开发贝叶斯方法的三个主题的统计分析复杂的调查：（一）调查分析的抽样单位有不同的纳入概率;（二）处理调查中的单位和项目无响应;和（三）使用旋转面板设计收集的样本的分析。 这项研究得到了方法、测量和统计计划以及联邦统计机构联盟的支持，该联盟隶属于调查和统计方法资助机会研究。