JOURNAL ARTICLE

Robust estimation of the false discovery rate

Stanley PoundsCheng Cheng

Year: 2006 Journal:   Bioinformatics Vol: 22 (16)Pages: 1979-1987   Publisher: Oxford University Press

Abstract

Abstract Motivation: Presently available methods that use p-values to estimate or control the false discovery rate (FDR) implicitly assume that p-values are continuously distributed and based on two-sided tests. Therefore, it is difficult to reliably estimate the FDR when p-values are discrete or based on one-sided tests. Results: A simple and robust method to estimate the FDR is proposed. The proposed method does not rely on implicit assumptions that tests are two-sided or yield continuously distributed p-values. The proposed method is proven to be conservative and have desirable large-sample properties. In addition, the proposed method was among the best performers across a series of ‘real data simulations’ comparing the performance of five currently available methods. Availability: Libraries of S-plus and R routines to implement the method are freely available from Contact: [email protected] Supplementary information: Supplementary data are avilable at Bioinformatics online.

Keywords:
False discovery rate Computer science Multiple comparisons problem Simple (philosophy) Series (stratigraphy) Algorithm Estimation Data mining Mathematics Statistics

Metrics

221
Cited By
11.32
FWCI (Field Weighted Citation Impact)
29
Refs
0.99
Citation Normalized Percentile
Is in top 1%
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Citation History

Topics

Statistical Methods in Clinical Trials
Physical Sciences →  Mathematics →  Statistics and Probability
Statistical Methods and Bayesian Inference
Physical Sciences →  Mathematics →  Statistics and Probability
Statistical Methods and Inference
Physical Sciences →  Mathematics →  Statistics and Probability

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