题目：Change-detection-assisted multiple testing for spatiotemporal data
报告人：Prof. Lilun Du (香港科技大学）
摘要：We consider large-scale multiple testing of data with spatially and temporally clustered signals. When the conventional false discovery rate (FDR) procedure is applied without taking into account the clustering structure, the power to detect statistical significance tends to be reduced. We formulate a spatiotemporal framework in the presence of multiple change points for multiple testing, and propose a data-driven procedure that aims to fully utilize the clustering information. With the aim of grouping data into several sets, we develop a new change-point detection algorithm that integrates the kernel-based aggregation of spatial observations with a global loss function at the temporal level. Then, we derive an FDR control scheme for set-wise multiple testing. Under some mild conditions on the spatiotemporal dependence structure, the FDR is shown to be strongly controlled. Theoretical analysis and numerical studies demonstrate the advantages of our algorithm over competing methods.