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Parametric and nonparametric FDR estimation revisited.

Baolin Wu·Zhong Guan·Hongyu Zhao
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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.

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Bayes TheoremBiometryFinite Element AnalysisGene Expression ProfilingGenomicsHumansLeukemiaModels, StatisticalOligonucleotide Array Sequence AnalysisProteomicsStatistics, NonparametricJournal ArticleResearch Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, Non-P.H.S.

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Baolin Wu, Zhong Guan, Hongyu Zhao. 2006. Parametric and nonparametric FDR estimation revisited.. https://doi.org/10.1111/j.1541-0420.2006.00531.x

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