Collaborative Research: Modernizing Mixed Model Prediction

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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.