Title:

Testing and estimation for clustered signals

Abstract: 

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.

Short bio: 

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 causal inference. She currently serves as associate editor of Biometrics.