CDfdr: A Comparison Density Approach to Local False Discovery Rate Estimation
This work provides a theoretical unification for statisticians and researchers in multiple testing, though it appears incremental as it builds on prior methods without introducing a new paradigm.
The paper tackles the problem of unifying empirical Bayes and frequentist false discovery rate methods by introducing a comparison density approach, resulting in a framework that integrates existing local false discovery rate methods under a single concept and notation.
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.