TY - JOUR
AU1 - Liang, Faming
AU2 - Zhang, Jian
AB - Testing of multiple hypotheses involves statistics that are strongly dependent in some applications, but most work on this subject is based on the assumption of independence. We propose a new method for estimating the false discovery rate of multiple hypothesis tests, in which the density of test scores is estimated parametrically by minimizing the KullbackLeibler distance between the unknown density and its estimator using the stochastic approximation algorithm, and the false discovery rate is estimated using the ensemble averaging method. Our method is applicable under general dependence between test statistics. Numerical comparisons between our method and several competitors, conducted on simulated and real data examples, show that our method achieves more accurate control of the false discovery rate in almost all scenarios.
TI - Estimating the false discovery rate using the stochastic approximation algorithm
JF - Biometrika
DO - 10.1093/biomet/asn036
DA - 2008-12-26
UR - https://www.deepdyve.com/lp/oxford-university-press/estimating-the-false-discovery-rate-using-the-stochastic-approximation-f0QCac80wU
SP - 961
EP - 977
VL - 95
IS - 4
DP - DeepDyve
ER -