Year: 2023
Type: article
Abstract: Two-sample multiple testing problems of sparse spatial data are frequently arising in a variety of scientific applications.In this article, we develop a novel neighborhood-assisted and posterior-adjusted (NAPA) approach to incorporate both the spatial smoothness and sparsity type side information to improve the power of the test while controlling the false discovery of multiple testing.We translate the side information into a set of weights to adjust the p-values, where the spatial pattern is encoded by the ordering of the locations, and the sparsity structure is encoded by a set of auxiliary covariates.We establish the theoretical properties of the proposed test, including the guaranteed power improvement over some stateof-the-art alternative tests, and the asymptotic false discovery control.We demonstrate the efficacy of the test through intensive simulations and two neuroimaging applications.
Source: Statistica Sinica
Institutions: Fudan University, University of California, Berkeley
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4388415700