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Testing and estimation for clustered signals

发布者:赵斯达发布时间:2019-12-23浏览次数:474

报告题目:Testing and estimation for clustered signals

时间:2019年12月25日 10:30-11:30

地点:网络真人赌钱游戏平台范孙楼116教室

报告摘要:

We propose a change-point detection method for large scale multiple testing problems with data having clustered signals. Unlike the classic change-point setup, the signals can vary in size within a cluster. The clustering structure on the signals enables us to effectively delineate the boundaries between signal and non-signal segments. New test statistics are proposed for observations from one and/or multiple realizations. Their asymptotic distributions are derived. We also study the associated variance estimation problem. We allow the variances to be heteroscedastic in the multiple realization case, which substantially expands the applicability of the proposed method. Simulation studies demonstrate that the proposed approach has a favorable performance. Our procedure is applied to an array CGH dataset.

报告人:

Hongyu Cao,Ph.D.

报告人简介:

Hongyuan Cao is currently an associate professor of statistics at Florida State University. She got her Ph.D in statistics from UNC-Chapel Hill. Her research interests include high dimensional data, machine learning, longitudinal data analysis, survival analysis and applications in medicine, biological sciences and social sciences. She currently serves as associate editor of Biometrics.


邀请人:邹长亮 教授