False discovery rate: setting the probability of false claim of detection

False discovery rate : setting the probability of false claim of detection

When testing multiple hypothesis in a survey –e.g. many different source locations, template waveforms, and so on– the final result consists in a set of confidence intervals, each one at a desired confidence level. But the probability that at least one of these intervals does not cover the true value increases with the number of trials. With a sufficiently large array of confidence intervals, o...

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M ay 2 00 5 False discovery rate : setting the probability of false claim of detection

When testing multiple hypothesis in a survey –e.g. many different source locations, template waveforms, and so on– the final result consists in a set of confidence intervals, each one at a desired confidence level. But the probability that at least one of these intervals does not cover the true value increases with the number of trials. With a sufficiently large array of confidence intervals, o...

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qc / 0 50 40 49 v 3 2 8 Ju l 2 00 5 False discovery rate : setting the probability of false claim of detection

When testing multiple hypothesis in a survey –e.g. many different source locations, template waveforms, and so on– the final result consists in a set of confidence intervals, each one at a desired confidence level. But the probability that at least one of these intervals does not cover the true value increases with the number of trials. With a sufficiently large array of confidence intervals, o...

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Discovering the false discovery rate

Our work on the false discovery rate (FDR), and the paper Benjamini andHochberg (1995), has its origins in two papers concerned with multiple testing ofm hypotheses of which unknownm0 are true. First was Schweder and Spjøtvoll (1982), who suggested plotting the ranked p-values, assessingm0 via an eye-fitted line, and rejecting the otherm−m0 hypotheses. In Hochberg and Benjamini (1990) we develo...

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Robust estimation of the false discovery rate

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 ...

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False discovery rate: setting the probability of false claim of detection

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