题目：An Introduction to High Dimensional Aymptotics

主讲人：罗格斯大学 韩启阳副教授

主持人：西南财经大学统计学院 常晋源教授

时间：2024年6月17 - 19日 上午9:00-11:30 下午14:30-17:00

地点：西南财经大学光华校区光华楼10楼1003

报告摘要：

High dimensional asymptotics has emerged as a new theoretical paradigm to precise characterize the stochastic behavior of a large number of statistical estimators, finding a wide range of applications beyond the reach of standard theoretical methods. In these talks, we will briefly introduce three main theoretical approaches in this field. In the first part, we will discuss the leave-one-out method, originally introduced in the context of robust regression. In the second part, we will introduce a Gaussian process approach, currently known as the Convex Gaussian Min-Max Theorem framework. In the third part, we will discuss an algorithmic approach, known as the Approximate Message Passing method. We will provide both rigorous, theoretical foundations for these approaches, and illustrate the utility of these methods in some of the canonical statistical settings and the more recent interpolating estimators. Time permitting, I will also briefly discuss more recent theoretical developments in this field.

主讲人简介：

Qiyang Han is an Associate Professor of Statistics at Rutgers University. He received a Ph.D. in Statistics in 2018 from University of Washington under the supervision of Professor Jon A. Wellner. His research expands broadly in mathematical statistics and high dimensional probability, with a particular focus on empirical process theory and its applications to nonparametric and high dimensional statistics. He is a recipient of the NSF CAREER award in 2022, the Bernoulli Society New Researcher Award in 2023, and the David G.Kendall's Award in Mathematical Statistics in 2024.