报告时间：2016年12月16日（周五）上午10:00

报告地点： 京师学堂第七会议室

报 告 人： 曹宏媛，Assistant Professor of University of Missouri

报告题目：Change-point estimation: another look at multiple testing problems

报告摘要：We consider large scale multiple testing for data that have locally clustered signals. With this structure, we apply techniques from change-point analysis and propose a boundary detection algorithm so that the clustering information can be utilised. Consequently the precision of the multiple testing procedure is substantially improved. We study tests with independent as well as dependent p-values. Monte Carlo simulations suggest that the methods perform well with realistic sample sizes and show improved detection ability compared with competing methods. Our procedure is applied to a genome-wide association dataset of blood lipids.