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 based Comparative Genomic Hybridization (aCGH) dataset.
We are grateful to two referees for their many helpful comments. The research is partially supported by NSF and NIH.
"Testing and estimation for clustered signals." Bernoulli 28 (1) 525 - 547, February 2022. https://doi.org/10.3150/21-BEJ1355