October 20, 2020 Webinar
Joint Modeling Approaches for Longitudinal Studies
Cheng Yong Tang, PhD
Abstract:
In longitudinal studies, it is fundamentally important to understand the dynamics in the mean function, variance function, and correlations of the repeated or clustered measurements. We will discuss new joint mean-variance-correlation regression approaches for modeling continuous and discrete repeated measurements from longitudinal studies. By applying hyperspherical coordinates, we obtain an unconstrained interpretable parametrization of the correlation matrix. We then propose regression approaches to model the correlation matrix of the longitudinal measurements by exploiting the unconstrained parametrization. The proposed modeling framework is parsimonious, interpretable, flexible, and it automatically guarantees the resulting correlation matrix to be non-negative definite. Data examples and simulations support the effectiveness of the proposed approaches. This talk is based on joint works with Weiping Zhang and Chenlei Leng.
Short Bio:
Dr. Cheng Yong Tang is Associate Professor of Statistics and the Seymour Wolfbein Senior Research Fellow of Fox School of Business at Temple University. He earned his PhD in Statistics in 2008 from Iowa State University, after which he worked as Assistant and Associate Professor in the Department of Statistics and Applied Probability of National University of Singapore 2008‐2013. He is an Associate Editor of a few journals including the Journal of the American Statistical Association, Journal of Business Economic Statistics, and Statistica Sinica.
Dr Tang's research is on statistical methodology including high‐dimensional statistical methods, empirical likelihood and nonparametric methods. Dr Tang's research experience covers topics in data sciences, finance, econometrics, sampling survey statistics, and statistical learning. His research has been funded by the NSF and NIH.
Dr Tang is an Elected Member of the International Statistical Institute, a member of the American Statistical Association, a member of the Institute of Mathematical Statistics, and a lifetime member of the International Chinese Statistical Association.