Bounding the FDP in competition-based control of the FDR

TL;DR

This paper introduces new methods, TDC-SB and TDC-UB, that provide tighter upper bounds on the false discovery proportion in competition-based FDR control, improving upon existing approaches with both simulated and real data.

Contribution

The paper presents novel procedures for bounding the FDP in competition-based FDR control, offering significantly tighter bounds than previous methods.

Findings

TDC-SB and TDC-UB provide tighter FDP bounds than existing methods.

The new procedures perform well on both simulated and real datasets.

They improve the reliability of false discovery proportion estimates in FDR control.

Abstract

Competition-based approach to controlling the false discovery rate (FDR) recently rose to prominence when, generalizing it to sequential hypothesis testing, Barber and Cand\`es used it as part of their knockoff-filter. Control of the FDR implies that the, arguably more important, false discovery proportion is only controlled in an average sense. We present TDC-SB and TDC-UB that provide upper prediction bounds on the FDP in the list of discoveries generated when controlling the FDR using competition. Using simulated and real data we show that, overall, our new procedures offer significantly tighter upper bounds than ones obtained using the recently published approach of Katsevich and Ramdas, even when the latter is further improved using the interpolation concept of Goeman et al.