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

协作研究：针对复杂调查或人口普查数据的贝叶斯模型设置中基于随机化的机器学习方法

基本信息

批准号：
2215169
负责人：
Paul Parker
金额：
$ 33.73万
依托单位：
University of California-Santa Cruz
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2215169&HistoricalAwards=false
关键词：
Collaborative Research Randomization Based Machine

项目摘要

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.

官方的联邦统计系统数据通常具有复杂的抽样和设计特征，这限制了高级统计分析。这类调查和人口普查数据的例子是科学和工程研究生和博士后调查（GSS），获得博士学位的调查（SED），最近的大学毕业生全国调查（NSRCG）和大学毕业生全国调查（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、多重插补和数据隐私。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）具有价值。研究人员将对研究生和本科生进行教育和培训。该研究项目将使用基于随机化的方法，为复杂的调查或人口普查中的单位和地区数据开发贝叶斯统计和机器学习方法。该项目有三个目标。在目标1中，研究人员将重点关注使用数据简化方法对现有模型的几个扩展。具体而言，这一目标将利用随机投影技术，贝叶斯分层建模框架内，提供有用的工具，分析联邦数据。随后，在目标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交媒体、管理数据和其他结构化数据源的数据整合机制。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，在贝叶斯分层模型中对时间相关的复杂调查或人口普查数据进行建模。最终，该项目将开发出对科学界和联邦统计界都有用的原则性方法。该奖项反映了NSF的法定使命，并被认为值得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来支持。