# Multiple Hypothesis Testing Framework for Spatial Signals

arXiv: https://arxiv.org/abs/2108.12314 · HTML: https://mlsanity.com/paper/2108.12314/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 66 references that are in the map.
- First posted on arXiv 27 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [Can parametric statistical methods be trusted for fMRI based group studies?](https://mlsanity.com/paper/1511.01863/) (2015)
- [False discovery rate smoothing](https://mlsanity.com/paper/1411.6144/) (2014)
- [False discovery rate regression: an application to neural synchrony detection in primary visual cortex](https://mlsanity.com/paper/1307.3495/) (2013)
- [Contextual Online False Discovery Rate Control](https://mlsanity.com/paper/1902.02885/) (2019)
- [Multiple Testing for Neuroimaging via Hidden Markov Random Field](https://mlsanity.com/paper/1404.1371/) (2014)
- [On nonparametric estimation of a mixing density via the predictive recursion algorithm](https://mlsanity.com/paper/1812.02149/) (2018)
- [Detection under One-Bit Messaging over Adaptive Networks](https://mlsanity.com/paper/1803.06725/) (2018)
- [The p-filter: multi-layer FDR control for grouped hypotheses](https://mlsanity.com/paper/1512.03397/) (2015)

## Built on by (most cited first, 1 of 1)

- [On Large-Scale Multiple Testing Over Networks: An Asymptotic Approach](https://mlsanity.com/paper/2211.16059/) (2022)

## Abstract (arXiv)

The problem of identifying regions of spatially interesting, different or adversarial behavior is inherent to many practical applications involving distributed multisensor systems. In this work, we develop a general framework stemming from multiple hypothesis testing to identify such regions. A discrete spatial grid is assumed for the monitored environment. The spatial grid points associated with different hypotheses are identified while controlling the false discovery rate at a pre-specified level. Measurements are acquired using a large-scale sensor network. We propose a novel, data-driven method to estimate local false discovery rates based on the spectral method of moments. Our method is agnostic to specific spatial propagation models of the underlying physical phenomenon. It relies on a broadly applicable density model for local summary statistics. In between sensors, locations are assigned to regions associated with different hypotheses based on interpolated local false discovery rates. The benefits of our method are illustrated by applications to spatially propagating radio waves.