# Weighted False Discovery Rate Control in Large-Scale Multiple Testing

arXiv: https://arxiv.org/abs/1508.01605 · HTML: https://mlsanity.com/paper/1508.01605/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2015.

## Built on by (most cited first, 4 of 4)

- [Integrative conformal p-values for powerful out-of-distribution testing with labeled outliers](https://mlsanity.com/paper/2208.11111/) (2022)
- [A Locally Adaptive Algorithm for Multiple Testing with Network Structure](https://mlsanity.com/paper/2203.11461/) (2022)
- [Synthetic-Powered Multiple Testing with FDR Control](https://mlsanity.com/paper/2602.16690/) (2026)
- [Ranking by Lifts: A Cost-Benefit Approach to Large-Scale A/B Tests](https://mlsanity.com/paper/2407.01036/) (2024)

## Abstract (arXiv)

The use of weights provides an effective strategy to incorporate prior domain knowledge in large-scale inference. This paper studies weighted multiple testing in a decision-theoretic framework. We develop oracle and data-driven procedures that aim to maximize the expected number of true positives subject to a constraint on the weighted false discovery rate. The asymptotic validity and optimality of the proposed methods are established. The results demonstrate that incorporating informative domain knowledge enhances the interpretability of results and precision of inference. Simulation studies show that the proposed method controls the error rate at the nominal level, and the gain in power over existing methods is substantial in many settings. An application to genome-wide association study is discussed.