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False discovery rates and multiple testing

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Abstract

Statistical methods involving high-dimensional testing, i.e., a large number of simultaneous tests, have become important in recent days. Applications include microarrays, fMRI images and signal processing. Information that can be obtained by treating them as connected tests leads to the concept of discovery rates as well as to the Bayesian approach to hypothesis tests. It gives us great pleasure to honour Herbert Robbins who introduced the Empirical Bayes technique in statistical inference, which connects the frequentist and the Bayesian approaches in this problem.

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Authors and Affiliations

  1. Mohan Delampady, Statistics and Mathematics Unit Indian Statistical Institute, RVCE Post, Bangalore, 560 059, India

    Soumen Dey & Mohan Delampady

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  1. Soumen Dey
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  2. Mohan Delampady
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Correspondence to Soumen Dey.

Additional information

Soumen Dey is a research scholar at ISI, Bangalore.

Mohan Delampady is a Professor at the Indian Statistical Institute, Bangalore.

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Dey, S., Delampady, M. False discovery rates and multiple testing. Reson 18, 1095–1109 (2013). https://doi.org/10.1007/s12045-013-0137-9

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  • DOI: https://doi.org/10.1007/s12045-013-0137-9

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