Collaborative Research: Modernizing Mixed Model Prediction

社会许多领域的信息爆炸，从医学到经济学，从商业到社交媒体，导致了现代数据科学在学科层面知识方面的紧迫问题，例如精准医学，集中营销，家庭经济学和许多其他领域。其中包括重要应用领域的有效数据分析和预测方法，从通过差异隐私（DP）的隐私保护到专注于表观遗传标记预测的精准医学和公共卫生差异，以及使用美国就业数据的预测劳工统计局（BLS）。该项目旨在开发和采用称为混合模型预测的新方法。特别是，对于DP应用程序，研究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BLS应用程序，研究人员将针对有关COVID-19大流行期间波动性的问题，因此需要对传统方法进行强有力的修改。本研究将与专注于特定应用领域的合作者一起开展，主要研究三个方面：1）基因组预测问题中的多变量混合模型预测（MMP），其中相关的DNA甲基化标志物反映了潜在的疾病生物学，并通过借用这种多变量结构的力量来提高预测准确性; 2）用于差异私有（DP）数据的MMP，其中集群或分组身份被设计污染并且不被释放以保护隐私;以及3）具有非高斯随机效应和错误的MMP，这可以大大扩展MMP可以应用于不适合许多现代数据集的经典正态假设的情况范围。研究人员将为每个目标开发所需的方法，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，研究人员将与他们在主题领域的合作者密切合作，实施本项目中开发的方法，以回答实际问题。该奖项反映了NSF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。

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.