主题：Tensor Elliptical Graphical Model张量椭圆图模型
主讲人：南开大学冯龙教授
主持人：统计与数据科学学院兰伟教授
时间：2026年3月13日（周五）下午14：00-15：00
地点：柳林校区弘远楼408会议室
主办单位：统计与数据科学学院和统计研究中心 科研处
主讲人简介：
冯龙现任南开大学统计与数据科学学院教授、博士生导师。入选教育部青年人才计划、南开大学百名青年学科带头人。主要从事高维数据分析方面的研究，在统计学国际顶尖杂志JRSSB, JASA、Biometrika、Annals of Statistics、JOE、JBES等发表50余篇论文。主持一项天津市杰出青年基金、国家自然科学基金面上项目和青年项目。担任Statistical Theory and Related Field副主编。
内容提要：
We address the problem of robust estimation of sparse high dimensional tensor elliptical graphical model. Most of the research focus on tensor graphical model under normality. To extend the tensor graphical model to more heavy-tailed scenarios, motivated by the fact that up to a constant, the spatial-sign covariance matrix can approximate the true covariance matrix when the dimension turns to infinity under tensor elliptical distribution, we propose a spatial-sign-based estimator to robustly estimate tensor elliptical graphical model, the rate of which matches the existing rate under normality for a wider family of distribution, i.e. elliptical distribution. We also conducted extensive simulations and real data applications to illustrate the practical utility of the proposed methods, especially under heavy-tailed distribution.
