Powerful Spatial Multiple Testing via Borrowing Neighboring Information

TL;DR

This paper introduces 2d-SMT, a novel spatial multiple testing method that leverages neighboring information to control FDR and enhance detection power in spatial data analysis.

Contribution

The paper proposes the 2d-SMT procedure, a new approach that incorporates spatial dependence into multiple testing to improve FDR control and detection power.

Findings

2d-SMT effectively controls FDR under weak spatial dependence.

Combining 2d-SMT with weighted BH procedures improves power.

Numerical experiments show superior performance in FDR and power trade-off.

Abstract

Clustered effects are often encountered in multiple hypothesis testing of spatial signals. In this paper, we propose a new method, termed \textit{two-dimensional spatial multiple testing} (2d-SMT) procedure, to control the false discovery rate (FDR) and improve the detection power by exploiting the spatial information encoded in neighboring observations. The proposed method provides a novel perspective of utilizing spatial information by gathering signal patterns and spatial dependence into an auxiliary statistic. 2d-SMT rejects the null when a primary statistic at the location of interest and the auxiliary statistic constructed based on nearby observations are greater than their corresponding cutoffs. 2d-SMT can also be combined with different variants of the weighted BH procedures to improve the detection power further. A fast algorithm is developed to accelerate the search for optimal cutoffs in 2d-SMT. In theory, we establish the asymptotic FDR control of 2d-SMT under weak spatial dependence. Extensive numerical experiments demonstrate that the 2d-SMT method combined with various weighted BH procedures achieves the most competitive performance in FDR and power trade-off.