High-Dimensional Statistical Inference: From Vectors to Matrices
主 题: High-Dimensional Statistical Inference: From Vectors to Matrices
报告人: Prof. T. Tony Cai （ University of Pennsylvania）
时 间: 2012-06-21 14:00-15:00
地 点: 理科一号楼1114
Driven by a wide range of applications, high-dimensional statistical inference has seen significant developments over the last few years. These and other related problems have also attracted much interest in a number of fields including applied mathematics, engineering, and statistics. In this talk I will discuss some recent advances on several related problems in high-dimensional inference including compressed sensing, low-rank matrix recovery, and estimation of large covariance matrices. The connections as well as differences among these problems will be also discussed.
演讲人简介：蔡天文博士现任美国宾夕法尼亚大学沃顿商公司Dorothy Silberberg 统计学讲席教授，兼任该校应用数学及计算科学教授。他于1996年在康奈尔大学获得数学博士, 研究方向包括高维统计推断，大范围假设检验，非参数函数估计，函数数据分析，小波方法和应用，统计决策论, 发表了80多篇学术论文。由于出色科研成就，他于2008年获得世界统计学考普斯奖 (COPSS Presidents\' Award), 这是统计学界对于青年统计学家的最高嘉奖，肯定了他在数学和统计学理论与方法上深刻而广泛新发现，包括小波理论的应用、小波回归中区块阈值、优化理论、非参函数估计的适应性，小样本的置信区间、控制误判率的方法； 肯定了他把统计学理论应用到化学成分判别、医学成像和芯片数据分析等领域的尝试。他是《统计年刊》主编(2010-2012)，还在另外两个杂志担任编委。更多情况可见
http://www-stat.wharton.upenn.edu/~tcai
