CAREER: Super-Quantile Based Methods for Analyzing Large-Scale Heterogenous Data

职业：基于超分位数的大规模异构数据分析方法

• 批准号: 2238428
• 负责人: Kean Ming Tan
• 金额: $ 41.04万
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238428&HistoricalAwards=false
• 关键词: CAREER; Super; Quantile; Based; Methods

Data-driven decision-making has become ubiquitous across many scientific disciplines and in every aspect of life. As society relies more heavily on statistical modeling to make decisions, it becomes imperative for statistical methods to be able to model heterogenous data so that different decisions that will lead to better outcomes for different subgroups can be made. This is appealing in the big data era as data-rich settings allow statistical models to be trained to be more flexible and robust to heterogeneous data. This project focuses on developing a new class of statistical methods for modeling large-scale heterogenous data based on the super-quantile (tail average), also referred to as the expected shortfall or conditional value-at-risk. The methods under development will have the potential to answer questions that cannot be directly answered previously using existing tools in different fields such as climate science, neuroscience, finance, and health disparity research. The project will allow undergraduate and graduate students to work on cutting-edge methods for modeling data heterogeneity. In addition, a graduate-level course on data heterogeneity will be developed. The investigator will also engage in K-12 educational outreach in collaboration with the Center for Educational Outreach at the University of Michigan.With the abundance of data, there has been growing interest in modeling large-scale heterogeneous data for decision-making. Quantile regression is one representative statistical tool for modeling heterogeneous data, but one limitation is that it focuses on a specific quantile level and may not be the best approach for answering scientific questions that involve aggregate information of the lower/upper tail of the distribution of interest. The investigator studies the development of a new class of super-quantile-based tools to help practitioners answer such questions. There are three aims in this project: (i) establish theoretical foundations and develop scalable computational algorithms for fitting super-quantile regression in the big-data regime, high-dimensional regime, and data with outliers; (ii) develop super-quantile regression methods in the presence of unmeasured confounders; (iii) develop a series of super-quantile-based methods for analyzing different data types. The investigator will create a comprehensive platform, including software and e-book tutorials, to encourage using super-quantile-based methods.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.

数据驱动的决策已经在许多科学学科和生活的各个方面变得无处不在。 随着社会越来越依赖统计模型来做出决策，统计方法必须能够对异构数据进行建模，以便做出不同的决策，从而为不同的子群体带来更好的结果。 这在大数据时代很有吸引力，因为数据丰富的设置允许训练统计模型，使其对异构数据更加灵活和鲁棒。该项目的重点是开发一类新的统计方法，用于基于超分位数（尾部平均值）（也称为预期缺口或条件风险价值）对大规模异构数据进行建模。正在开发的方法将有可能回答以前使用气候科学、神经科学、金融和健康差异研究等不同领域的现有工具无法直接回答的问题。 该项目将允许本科生和研究生研究数据异构性建模的尖端方法。 此外，还将开发关于数据异构性的研究生课程。 研究人员还将与密歇根大学教育外展中心合作，参与 K-12 教育外展工作。随着数据的丰富，人们对大规模异构数据建模以进行决策的兴趣日益浓厚。 分位数回归是一种用于对异构数据进行建模的代表性统计工具，但一个局限性是它专注于特定的分位数水平，并且可能不是回答涉及感兴趣分布的下/上尾部聚合信息的科学问题的最佳方法。 研究人员研究了一类基于超分位数的新型工具的开发，以帮助从业者回答此类问题。 该项目有三个目标：（i）建立理论基础并开发可扩展的计算算法，用于在大数据体系、高维体系和具有异常值的数据中拟合超分位数回归； (ii) 在存在无法测量的混杂因素的情况下开发超分位数回归方法； (iii) 开发一系列基于超分位数的方法来分析不同的数据类型。研究者将创建一个综合平台，包括软件和电子书教程，以鼓励使用基于超分位数的方法。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。