# A grouped, selectively weighted false discovery rate procedure

## TL;DR

This paper introduces a new grouped, selectively weighted FDR procedure called sGBH that improves power in structured hypotheses testing by focusing on relevant groups and selectively weighting p-values, without relying on a groupwise mixture model.

## Contribution

The paper proposes the sGBH method, which enhances FDR control by selectively identifying and weighting hypothesis groups, outperforming existing grouped weighted FDR procedures.

## Key findings

- sGBH demonstrates significantly improved power over GBH.
- The method maintains FDR control under simple conditions.
- Application to gene expression data shows practical effectiveness.

## Abstract

False discovery rate (FDR) control in structured hypotheses testing is an important topic in simultaneous inference. Most existing methods that aim to utilize group structure among hypotheses either employ the groupwise mixture model or weight all p-values or hypotheses. Thus, their powers can be improved when the groupwise mixture model is inappropriate or when most groups contain only true null hypotheses. Motivated by this, we propose a grouped, selectively weighted FDR procedure, which we refer to as "sGBH". Specifically, without employing the groupwise mixture model, sGBH identifies groups of hypotheses of interest, weights p-values in each such group only, and tests only the selected hypotheses using the weighted p-values. The sGBH subsumes a standard grouped, weighted FDR procedure which we refer to as "GBH". We provide simple conditions to ensure the conservativeness of sGBH, together with empirical evidence on its much improved power over GBH. The new procedure is applied to a gene expression study.