Collaborative Research: Design-Based Optimal Subdata Selection Using Mixture-of-Experts Models to Account for Big Data Heterogeneity

协作研究：基于设计的最佳子数据选择，使用专家混合模型来解释大数据异构性

• 批准号: 2210546
• 负责人: Min Yang
• 金额: $ 15万
• 依托单位: University of Illinois at Chicago
• 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: 2022-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210546&HistoricalAwards=false
• 关键词: Collaborative; Research; Design; Based; Optimal

With technological advances, it has become easy to collect massive amounts of data for most areas of research. But with the size of datasets measured in terabytes or even petabytes, analyzing such datasets can become an expensive computational challenge and may be impossible on a typical desktop or laptop computer. However, for making impactful discoveries, it may be unnecessary to analyze an entire dataset. Consequently, there is great interest in developing and studying methods for selecting a subset from a massive dataset and for drawing conclusions based on the much smaller selected dataset. Such methods are known as subdata selection or subsampling methods. One obvious subsampling method consists of randomly selecting data from the entire dataset. While this is often the simplest and fastest option, it has been established that better options are often available. In this project, the principal investigators (PIs) aim to develop and study a rigorous framework and new methods for optimal subdata selection by using models that account for heterogeneity in the data, which is often present in large datasets. Research findings will be incorporated in topical courses to train graduate students in large-scale data analysis. The work will also be disseminated via the PIs’ collaborations in public health, biomedical science, and business.Rather than assuming a multiple regression model, the PIs plan to develop and study subdata selection methods based on mixture-of-experts (ME) models, which can account for heterogeneity in the data. The PIs will initially develop and study subdata selection methods for a subclass of the ME models, known as clusterwise linear regression models, for which the gate functions are constant. This will be followed by studying logistic-normal mixture models, in which the gate functions depend on the regression variables. For both cases, the investigators plan to develop information-based optimal subdata selection methods, first for continuous response variables and then for binary response variables, study their statistical properties, and develop efficient algorithms for the methods that will be made available in an R package.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.

随着技术进步，对于大多数研究领域，收集大量数据变得很容易。但是，随着数据集的大小在trabytes甚至pb bit中测量，分析此类数据集可能会成为昂贵的计算挑战，并且在典型的台式机或笔记本电脑上可能不可能成为一个昂贵的计算挑战。但是，对于有影响力的发现，可能不必分析整个数据集。因此，人们对开发和研究从大型数据集中选择子集的方法以及基于较小的选定数据集的结论进行了非常兴趣。此类方法称为子数据选择或亚采样方法。一种明显的子采样方法包括从整个数据集中随机选择数据。尽管这通常是最简单，最快的选择，但已经确定通常可以使用更好的选择。在该项目中，主要研究人员（PIS）旨在通过使用解释数据中异质性的模型来开发和研究一种严格的框架和新方法，以选择最佳子数据，这通常是在大型数据集中存在的。研究结果将纳入局部课程，以培训研究生大规模数据分析。这项工作还将通过PIS在公共卫生，生物医学科学和商业方面的合作进行传播。 PIS最初将开发和研究ME模型子类的子数据选择方法，称为群集线性回归模型，栅极函数是恒定的。随后将研究逻辑正常混合模型，其中门函数取决于回归变量。在这两种情况下，研究人员都计划开发基于信息的最佳子数据选择方法，首先是连续响应变量，然后是二进制响应变量，研究其统计属性，并为将在R套件中提供的方法开发有效的算法。该奖项将对NSF的法定任务进行了评估，并通过评估了Infestia the Infectia the Intellitia the Infectial andiria this Infectia infortial and Foundlit anditial and Foundliatial and Foundlial and Foundlit and Founlitial的支持。