# False discovery rate smoothing

arXiv: https://arxiv.org/abs/1411.6144 · HTML: https://mlsanity.com/paper/1411.6144/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2014.

## Built on by (most cited first, 8 of 8)

- [Lattice partition recovery with dyadic CART](https://mlsanity.com/paper/2105.13504/) (2021)
- [Variance estimation in graphs with the fused lasso](https://mlsanity.com/paper/2207.12638/) (2022)
- [Multiple Hypothesis Testing Framework for Spatial Signals](https://mlsanity.com/paper/2108.12314/) (2021)
- [DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data](https://mlsanity.com/paper/2310.13349/) (2023)
- [Treatment response as a latent variable](https://mlsanity.com/paper/2502.08776/) (2025)
- [Controlling False Discovery in Arbitrarily Structured Hypothesis Spaces via Reproducing Kernels](https://mlsanity.com/paper/2605.17559/) (2026)
- [A False Discovery Rate Control Method Using a Fully Connected Hidden Markov Random Field for Neuroimaging Data](https://mlsanity.com/paper/2505.20688/) (2025)
- [Finite Resources False Discovery Rate Control in Structured Hypothesis Spaces](https://mlsanity.com/paper/2606.15393/) (2026)

## Abstract (arXiv)

We present false discovery rate smoothing, an empirical-Bayes method for exploiting spatial structure in large multiple-testing problems. FDR smoothing automatically finds spatially localized regions of significant test statistics. It then relaxes the threshold of statistical significance within these regions, and tightens it elsewhere, in a manner that controls the overall false-discovery rate at a given level. This results in increased power and cleaner spatial separation of signals from noise. The approach requires solving a non-standard high-dimensional optimization problem, for which an efficient augmented-Lagrangian algorithm is presented. In simulation studies, FDR smoothing exhibits state-of-the-art performance at modest computational cost. In particular, it is shown to be far more robust than existing methods for spatially dependent multiple testing. We also apply the method to a data set from an fMRI experiment on spatial working memory, where it detects patterns that are much more biologically plausible than those detected by standard FDR-controlling methods. All code for FDR smoothing is publicly available in Python and R.