Testing and estimation of clustered signals

TL;DR

This paper introduces a novel change-point detection method tailored for large-scale multiple testing with clustered signals, accommodating varying signal sizes and heteroscedastic variances, validated through simulations and genomic data application.

Contribution

The paper develops new test statistics and asymptotic theory for detecting clustered signals with heteroscedastic variances in large datasets.

Findings

Proposed method effectively detects clustered signals in simulations.

Method performs well on aCGH genomic dataset.

Handles heteroscedastic variances across multiple realizations.

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 based Comparative Genomic Hybridization (aCGH)} dataset.