More powerful multiple testing under dependence via randomization

05/18/2023
by   Ziyu Xu, et al.
0

We show that two procedures for false discovery rate (FDR) control – the Benjamini-Yekutieli (BY) procedure for dependent p-values, and the e-Benjamini-Hochberg (e-BH) procedure for dependent e-values – can both be improved by a simple randomization involving one independent uniform random variable. As a corollary, the Simes test under arbitrary dependence is also improved. Importantly, our randomized improvements are never worse than the originals, and typically strictly more powerful, with marked improvements in simulations. The same techniques also improve essentially every other multiple testing procedure based on e-values.

READ FULL TEXT

page 16

page 17

page 18

page 27

page 28

research
03/16/2018

False discovery rate control for multiple testing based on p-values with càdlàg distribution functions

For multiple testing based on p-values with càdlàg distribution function...
research
10/06/2021

Deploying the Conditional Randomization Test in High Multiplicity Problems

This paper introduces the sequential CRT, which is a variable selection ...
research
01/23/2022

Elementary proofs of four standard results on false discovery rate

We collect self-contained elementary proofs of four standard results in ...
research
12/19/2022

Multiple testing under negative dependence

The multiple testing literature has primarily dealt with three types of ...
research
06/04/2019

On Benjamini-Hochberg procedure applied to mid p-values

Multiple testing with discrete p-values routinely arises in various scie...
research
07/28/2020

Admissible ways of merging p-values under arbitrary dependence

Methods of merging several p-values into a single p-value are important ...
research
02/14/2023

Derandomized Novelty Detection with FDR Control via Conformal E-values

Conformal prediction and other randomized model-free inference technique...

Please sign up or login with your details

Forgot password? Click here to reset