威斯康星大学张春明教授系列学术报告2

报告题目：Large-Scale Multiple Testing under Dependence via Graphical Models

报 告 人：Chunming Zhang, Professor of Statistics, University of Wisconsin-Madison

照片：

邀 请 人：冶 继 民 教 授

报告时间：2020年6月11号上午9:00:-10:30

报告平台：Zoom ID:895 4593 9701（密码4SM80j）

报告人简介：张春明，美国威斯康星大学国际著名统计学教授，统计学四大顶级期刊之一Annals of Statistics的前副主编、Journal of the American Statistical Association的现任副主编。在统计学四大顶级期刊发表论文10余篇，主要研究领域有：Statistical learning & data mining；Statistical methods with applications to imaging neuroinformatics and bioinformatics；Multiple testing; large-scale simultaneous inference and applications；Statistical methods in financial econometrics；Non- and semi-parametric estimation & inference；Functional & longitudinal data analysis.

报告摘要：Large-scale multiple testing tasks in applications often exhibit dependence, and leveraging the dependence among individual tests is an important but challenging problem in statistics. In this talk, I will present some recent work on multiple testing procedures under dependence via graphical models with applications to a genome-wide association study (GWAS).