CDfdr: A Comparison Density Approach to Local False Discovery Rate Estimation

TL;DR

This paper introduces a unified approach to local false discovery rate estimation using comparison density, connecting various existing methods under a common framework to enhance understanding and application.

Contribution

It unifies existing local fdr methods through the concept of comparison density, bridging different models under a single theoretical framework.

Findings

Reveals how existing local fdr methods fit into the comparison density framework

Provides a unified notation for false discovery rate estimation methods

Enhances understanding of the relationship between different false discovery approaches

Abstract

Efron et al. (2001) proposed empirical Bayes formulation of the frequentist Benjamini and Hochbergs False Discovery Rate method (Benjamini and Hochberg,1995). This article attempts to unify the `two cultures' using concepts of comparison density and distribution function. We have also shown how almost all of the existing local fdr methods can be viewed as proposing various model specification for comparison density - unifies the vast literature of false discovery methods under one concept and notation.