报告题目： Semiparametric Elliptical Mixture Clustering for High Dimensional Data

报告人：冯龙，南开大学

报告时间： 2026-5-8（星期五）10：00-11：30

报告地点：西安交通大学数学楼2-3会议室

报告摘要： Clustering high-dimensional data is especially challenging when cluster distributions are heavy tailed and only approximately elliptical. Existing high-dimensional methods are largely built for Gaussian or other light-tailed models, whereas classical robust elliptical procedures are mostly low dimensional or fully parametric. We propose a semiparametric elliptical mixture clustering framework with cluster-specific centers, an unknown common radial generator, and a common sparse precision-shape matrix, together with a data-driven rule for selecting the number of clusters. The method avoids specifying a parametric radial family and remains computationally feasible in high dimensions. We establish high-dimensional consistency for the estimated model components and the excess misclustering error. Simulations and a handwritten-digit application show strong performance, particularly under heavy-tailed elliptical components.

报告人简介：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、AOS、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。