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In this paper, we approach the variance changepoints detection problem from a multiple testing setting. After proving that the standard Step-Up and Step-Down procedures are inadmissible for the standa...
A topological multiple testing approach to peak detection is proposed for the problem of detecting transcription factor binding sites in ChIP-Seq data. After kernel smoothing of the tag counts over th...
Test statistics are often strongly dependent in large-scale multiple testing applications. Most corrections for multiplicity are unduly conservative for correlated test statistics, resulting in a loss...
We study statistical detection of grayscale objects in noisy images. The object of interest is of unknown shape and has an unknown intensity, that can be varying over the object and can be negative. N...
When testing a large number of independent hypotheses, three different questions are of interest: are some hypotheses true alternatives? How many of them? Which of them? These questions give rise to...
We investigate the asymptotic optimality of a large class of multiple testing rules using the framework of Bayesian Decision Theory. We consider a parametric setup, in which observations come from a...
An important estimation problem that is closely related to largescale multiple testing is that of estimating the null density and the proportion of nonnull effects. A few estimators have been introd...
A sequential multiple testing procedure recently introduced by Heinrich, Bach and Kornmeier allows to “zoom in” on, and thus identify regions with highly significant departures from null-hypotheses....
An important aspect of multiple hypothesis testing is controlling the significance level, or the level of Type I error. When the test statistics are not independent it can be particularly challengin...
The positive false discovery rate (pFDR) is a useful overall measure of errors for multiple hypothesis testing, especially when the underlying goal is to attain one or more discoveries. Control of p...

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