# Online Control of the False Discovery Rate under "Decision Deadlines"

arXiv: https://arxiv.org/abs/2110.01583 · HTML: https://mlsanity.com/paper/2110.01583/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 4 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [Online Rules for Control of False Discovery Rate and False Discovery Exceedance](https://mlsanity.com/paper/1603.09000/) (2016)
- [Online control of the false discovery rate with decaying memory](https://mlsanity.com/paper/1710.00499/) (2017)
- [SAFFRON: an adaptive algorithm for online control of the false discovery rate](https://mlsanity.com/paper/1802.09098/) (2018)
- [Online Control of the False Coverage Rate and False Sign Rate](https://mlsanity.com/paper/1905.01059/) (2019)
- [ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls](https://mlsanity.com/paper/1905.11465/) (2019)
- [Asynchronous Online Testing of Multiple Hypotheses](https://mlsanity.com/paper/1812.05068/) (2018)
- [Online control of the familywise error rate](https://mlsanity.com/paper/1910.04900/) (2019)
- [On Online Control of False Discovery Rate](https://mlsanity.com/paper/1502.06197/) (2015)
- [The Power of Batching in Multiple Hypothesis Testing](https://mlsanity.com/paper/1910.04968/) (2019)
- [Dynamic Algorithms for Online Multiple Testing](https://mlsanity.com/paper/2010.13953/) (2020)

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

- [Online multiple testing with e-values](https://mlsanity.com/paper/2311.06412/) (2023)

## Abstract (arXiv)

Online testing procedures aim to control the extent of false discoveries over a sequence of hypothesis tests, allowing for the possibility that early-stage test results influence the choice of hypotheses to be tested in later stages. Typically, online methods assume that a permanent decision regarding the current test (reject or not reject) must be made before advancing to the next test. We instead assume that each hypothesis requires an immediate preliminary decision, but also allows us to update that decision until a preset deadline. Roughly speaking, this lets us apply a Benjamini-Hochberg-type procedure over a moving window of hypotheses, where the threshold parameters for upcoming tests can be determined based on preliminary results. Our method controls the false discovery rate (FDR) at every stage of testing, as well as at adaptively chosen stopping times. These results apply even under arbitrary p-value dependency structures.