title: Parametric and nonparametric FDR estimation revisited.
abstract: Nonparametric and parametric approaches have been proposed to estimate false discovery rate under the independent hypothesis testing assumption. The parametric approach has been shown to have better performance than the nonparametric approaches. In this article, we study the nonparametric approaches and quantify the underlying relations between parametric and nonparametric approaches. Our study reveals the conservative nature of the nonparametric approaches, and establishes the connections between the empirical Bayes method and p-value-based nonparametric methods. Based on our results, we advocate using the parametric approach, or directly modeling the test statistics using the empirical Bayes method.
authors.affiliation: Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455, USA. baolin@biostat.umn.edu
authors.forename: Baolin
authors.initials: B
authors.lastname: Wu
authors.forename: Zhong
authors.initials: Z
authors.lastname: Guan
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
mesh_terms: D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D020342:Finite Element Analysis; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D023281:Genomics / Q000706:statistics & numerical data*; D006801:Humans; D007938:Leukemia / Q000235:genetics; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D040901:Proteomics / Q000706:statistics & numerical data; D018709:Statistics, Nonparametric