Collaborative Research: Modernizing Mixed Model Prediction

合作研究：现代化混合模型预测

• 批准号: 2210372
• 负责人: Thuan Nguyen
• 金额: $ 12.19万
• 依托单位: Oregon Health & Science University
• 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=2210372&HistoricalAwards=false
• 关键词: Collaborative; Research; Modernizing; Mixed; Model

The information explosion in many areas of society, from medicine to economics and business to social media, has resulted in pressing questions for modern data science regarding subject-level knowledge, such as in precision medicine, focused marketing, family economics, and many other areas. These include effective methods for data analysis and prediction in important areas of application ranging from privacy protection via differential privacy (DP) to precision medicine and public health disparities focusing on the prediction of epigenetic markers, and to predictions with employment data from the U.S. Bureau of Labor Statistics (BLS). This project aims to develop and employ new methods known as mixed model prediction. Particularly, for the DP application, the investigators will apply the methods to the publicly released 2020 U.S. decennial census; for the BLS application the investigators will target questions regarding volatility during the ongoing COVID-19 pandemic that thus require robust modifications from traditional approaches. The research will be carried out in conjunction with collaborators who are immersed in a particular application area.In this project, the investigators will focus on three major aims: 1) multivariate mixed model prediction (MMP) in genomic prediction problems where correlated DNA methylation markers reflect underlying disease biology and improved prediction accuracy is possible by borrowing strength across this multivariate structure; 2) MMP for differentially private (DP) data in which cluster or grouping identities are contaminated by design and not released to protect privacy; and 3) MMP with non-Gaussian random effects and errors, which greatly can expand the range of circumstances in which MMP can be applied beyond the classical normality assumptions that do not fit many modern datasets. The investigators will develop the required methodology for each aim, study the procedures theoretically, and carry out extensive empirical simulation studies to compare the new methods with other methods. Furthermore, the investigators will work closely with their collaborators in the subject fields on implementing the methods developed in this project to answering practical questions.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.

从医学到经济，从商业到社交媒体，社会许多领域的信息爆炸，导致了现代数据科学在学科水平知识方面的紧迫问题，例如在精确医学、重点营销、家庭经济学和许多其他领域。这些方法包括在重要应用领域的数据分析和预测的有效方法，从隐私保护到差异隐私(DP)，到精准医学和公共卫生差异，重点是表观遗传标记的预测，以及使用美国劳工统计局(BLS)的就业数据进行预测。该项目旨在开发和应用被称为混合模型预测的新方法。特别是，对于DP应用程序，调查人员将把这些方法应用于公开发布的2020年美国十年一次的人口普查；对于劳工统计局的应用程序，调查人员将针对正在进行的新冠肺炎大流行期间的波动性问题，因此需要对传统方法进行强有力的修改。这项研究将与沉浸在特定应用领域的合作者一起进行。在这个项目中，研究人员将集中在三个主要目标上：1)基因组预测问题中的多变量混合模型预测，其中相关的DNA甲基化标记反映潜在的疾病生物学，并且通过在这种多变量结构中借用强度来提高预测精度；2)用于差异隐私(DP)数据的多变量混合模型预测，在该数据中，集群或分组身份被设计污染，并且不被发布以保护隐私；3)具有非高斯随机效应和误差的最小二乘法，它可以极大地扩展最小二乘法的应用范围，使其可以超越经典的正态假设，这些假设不适合许多现代数据集。研究人员将为每个目标制定所需的方法，从理论上研究程序，并进行广泛的实证模拟研究，将新方法与其他方法进行比较。此外，调查人员将与他们在学科领域的合作者密切合作，实施在这个项目中开发的方法来回答实际问题。这个奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。