报告时间：2021年11月5日

                上午10:00-11:00

报告地点：腾讯会议

（会议ID：467213590）

报告嘉宾：张菁菲

报告主题：Gaussian Graphical Regression Models with High Dimensional Responses and Covariates

报告摘要

Gaussian Graphical Regression Models with High Dimensional Responses and Covariates

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.

个人简介

张菁菲，美国迈阿密大学herbert商学院管理科学系终身教授，博士生导师。于南开大学获得学士学位，美国伊利诺伊大学香槟分校获得博士学位。科研方向包括高维网络数据分析，矩阵以及张量数据分析，点过程模型。论文发表于Journal of the American Statistical Association, Journal of Machine Learning Reasech等。现任Statistica Sinica副主编。