Collaborative Research: Randomization Based Machine Learning Methods in a Bayesian Model Setting for Data From a Complex Survey or Census

联邦统计系统的官方数据通常具有复杂的抽样和设计特征，限制了高级统计分析。此类调查和普查数据的例子有：科学与工程研究生和博士后调查（GSS）、获得博士学位调查（SED）、全国近期大学毕业生调查（NSRCG）和全国大学毕业生调查（NSCG）。该项目将开发针对这些类型的联邦数据量身定制的贝叶斯统计和机器学习方法，以提高计算效率，并推进这些方法，以实现数据集成、多重输入和数据隐私。重要的是，这项研究的结果将对政府机构的工作以及处理复杂数据的许多主题学科（包括人口统计学、计量经济学和政治学等）有价值。软件包将被开发并向公众开放，调查人员将对研究生和本科生进行教育和培训。使用基于随机化的方法，该研究项目将开发贝叶斯统计和机器学习方法，用于复杂调查或人口普查中的单位和区域级数据。这个项目有三个目的。在目标1中，研究人员将重点关注使用数据约简方法对现有模型的几个扩展。具体来说，该目标将在贝叶斯分层建模框架内利用随机投影技术，为分析联邦数据提供有用的工具。随后，在Aim 2中，研究人员将利用随机权重前馈神经网络作为复杂调查数据的贝叶斯非线性回归工具的广泛适用性。这种方法将包括使用社会媒体、管理数据和其他结构化数据源进行数据集成的机制。最后，在目标3中，研究人员将使用递归神经网络及其随机权重变量作为工具，在贝叶斯层次模型中对时间相关的复杂调查或普查数据进行建模。最终，该项目将开发对科学和联邦统计社区都有用的原则性方法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Official federal statistical system data often have complex sampling and design features that limit advanced statistical analyses. Examples of such surveys and census data are the Survey of Graduate Students and Postdoctorates in Science and Engineering (GSS), the Survey of Earned Doctorates (SED), the National Survey of Recent College Graduates (NSRCG), and the National Survey of College Graduates (NSCG). This project will develop Bayesian statistical and machine learning methods that are tailored to these types of federal data to improve computational efficiency and advance these methods to allow for data integration, multiple imputation, and data privacy. Importantly, the results of this research will be of value to the work of government agencies as well as within many subject-matter disciplines that deal with complex data, including demography, econometrics, and political science, among others. Software packages will be developed and made publicly available, and the investigators will educate and train both graduate and undergraduate students.Using a randomization-based approach, this research project will develop Bayesian statistical and machine learning methodologies for unit- and area-level data from a complex survey or census. This project has three aims. In Aim 1 the investigators will focus on several extensions to existing models using data reduction methods. Specifically, this aim will leverage random projection techniques, within a Bayesian hierarchical modeling framework, to provide useful tools for analyzing federal data. Subsequently, in Aim 2, the investigators will take advantage of the wide-applicability of random weight feed-forward neural networks as a Bayesian nonlinear regression tool for complex survey data. This approach will include mechanisms for data integration using social media, administrative data, and other structured data sources. Finally, in Aim 3, the investigators will use recurrent neural networks and their random weight variants as a tool to model temporally correlated complex survey or census data within a Bayesian hierarchical model. Ultimately, this project will develop principled methodologies that are useful for both the scientific and federal statistical communities.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.