当前位置: Bwin国际 > 学术活动 > 正文
Gaussian Graphical Regression Models with High Dimensional Responses and Covariates
时间:2023年05月15日 13:47 点击数:






Though Gaussian graphical models have been widely used in many scientific fields, relatively limited progress has been made to link graph structures to external covariates. We propose a Gaussian graphical regression model, which regresses both the mean and the precision matrix of a Gaussian graphical model on covariates. In the context of co-expression quantitative trait locus (QTL) studies, our method can determine how genetic variants and clinical conditions modulate the subject-level network structures, and recover both the population-level and subject-level gene networks. Our framework encourages sparsity of covariate effects on both the mean and the precision matrix. In particular for the precision matrix, we stipulate simultaneous sparsity, i.e., group sparsity and element-wise sparsity, on effective covariates and their effects on network edges, respectively. We establish variable selection consistency first under the case with known mean parameters and then a more challenging case with unknown means depending on external covariates, and establish in both cases the l2 convergence rates of the estimated precision parameters. The utility and efficacy of our proposed method is demonstrated through simulation studies and an application to a co-expression QTL study with brain cancer patients.


Dr. Emma Jingfei Zhang is an Associate Professor of Management Science at the Miami Herbert Business School of the University of Miami, where she also holds a secondary appointment in the Department of Public Health Sciences at the Miller School of Medicine. Dr. Zhang received her Ph.D. in Statistics from the University of Illinois at Urbana-Champaign. Her research focuses on the developments of statistical methods and theory for high-dimensional networks, graphs, tensors, and point processes, with applications in business, social sciences and medicine.

©2019 东北师范大学Bwin国际 版权所有

地址:吉林省长春市人民大街5268号 邮编:130024 电话:0431-85099589 传真:0431-85098237