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Yoav Benjamini
Researcher at Tel Aviv University
Publications - 190
Citations - 114920
Yoav Benjamini is an academic researcher from Tel Aviv University. The author has contributed to research in topics: False discovery rate & Multiple comparisons problem. The author has an hindex of 53, co-authored 185 publications receiving 99592 citations. Previous affiliations of Yoav Benjamini include University of Washington & University of Pennsylvania.
Papers
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Journal ArticleDOI
Controlling the false discovery rate: a practical and powerful approach to multiple testing
Yoav Benjamini,Yosef Hochberg +1 more
TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
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The control of the false discovery rate in multiple testing under dependency
Yoav Benjamini,Daniel Yekutieli +1 more
TL;DR: In this paper, it was shown that a simple FDR controlling procedure for independent test statistics can also control the false discovery rate when test statistics have positive regression dependency on each of the test statistics corresponding to the true null hypotheses.
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Controlling the false discovery rate in behavior genetics research
TL;DR: The False Discovery Rate (FDR) is the expected proportion of false discoveries among the discoveries, and controlling the FDR goes a long way towards controlling the increased error from multiplicity while losing less in the ability to discover real differences.
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More powerful procedures for multiple significance testing.
Yosef Hochberg,Yoav Benjamini +1 more
TL;DR: Some new, general and simple procedures are discussed and demonstrated by two examples from the medical literature: the neuropsychologic effects of unidentified childhood exposure to lead, and the sleep patterns of sober chronic alcoholics.
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Adaptive linear step-up procedures that control the false discovery rate
TL;DR: In this article, a two-stage adaptive procedure is proposed to control the false discovery rate at the desired level q. This framework enables us to study analytically the properties of other procedures that exist in the literature.