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We propose a linear-time, single-pass, top-down algorithm for multiple testing on directed acyclic graphs (DAGs), where nodes represent hypotheses and edges specify a partial ordering in which hypotheses must be tested.
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Das maximale signifikanzniveau des tests: “Lehne H 0 {H}_{0} ab, wenn k k unter n n gegebenen tests zur ablehnung führen”
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Benjamini, Y · 1995
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Golub, T. R · 1999
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Gene ontology: tool for the unification of biology
Ashburner, M · 2000
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The control of the false discovery rate in multiple testing under dependency
Benjamini, Y · 2001
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Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach
Storey, J · 2004
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Two simple sufficient conditions for FDR control
Blanchard, G · 2008
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α \alpha -investing: a procedure for sequential control of expected false discoveries
Foster, D. P · 2008
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Multiple testing on the directed acyclic graph of gene ontology
Goeman, J. J · 2008
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Globalancova: exploration and assessment of gene group effects
Hummel, M · 2008
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Hierarchical testing of variable importance
Meinshausen, N · 2008
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Testing hypotheses in order
Rosenbaum, P. R · 2008
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Hierarchical false discovery rate–controlling methodology
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Golub, T · 2016
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Sequential selection procedures and false discovery rate control
G’Sell, M. G · 2016
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Global testing of differential gene expression
Hummel, M · 2016
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On procedures controlling the FDR for testing hierarchically ordered hypotheses
Lynch, G · 2016
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The control of the false discovery rate in fixed sequence multiple testing
Lynch, G · 2016
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STAR: a general interactive framework for FDR control under structural constraints
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Generalized α \alpha -investing: definitions, optimality results and application to public databases
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