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Adaptive and dynamic adaptive procedures for false discovery rate control and estimation
Author(s) -
Liang Kun,
Nettleton Dan
Publication year - 2012
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2011.01001.x
Subject(s) - false discovery rate , estimator , independence (probability theory) , mathematics , estimation , adaptive control , computer science , multiple comparisons problem , point estimation , sample size determination , algorithm , statistics , control (management) , artificial intelligence , biochemistry , chemistry , management , economics , gene
Summary. Many methods for estimation or control of the false discovery rate (FDR) can be improved by incorporating information about π 0 , the proportion of all tested null hypotheses that are true. Estimates of π 0 are often based on the number of p ‐values that exceed a threshold λ . We first give a finite sample proof for conservative point estimation of the FDR when the λ ‐parameter is fixed. Then we establish a condition under which a dynamic adaptive procedure, whose λ ‐parameter is determined by data, will lead to conservative π 0 ‐ and FDR estimators. We also present asymptotic results on simultaneous conservative FDR estimation and control for a class of dynamic adaptive procedures. Simulation results show that a novel dynamic adaptive procedure achieves more power through smaller estimation errors for π 0 under independence and mild dependence conditions. We conclude by discussing the connection between estimation and control of the FDR and show that several recently developed FDR control procedures can be cast in a unifying framework where the strength of the procedures can be easily evaluated.