Concept

Family-wise error rate

Related publications (58)

Confronting false discoveries in single-cell differential expression

Grégoire Courtine, Gioele La Manno, Quentin Barraud, Jordan Squair, Mark Andrew Anderson, Claudia Kathe, Nicholas David James, Matthieu Pierre Gautier, Michael Alexander Skinnider, Rémi Bernard René Hudelle, Thomas Haynes Hutson

Differential expression analysis of single-cell transcriptomics allows scientists to dissect cell-type-specific responses to biological perturbations. Here, the authors show that many commonly used methods are biased and can produce false discoveries. Diff ...

NATURE PORTFOLIO2021

A comprehensive error rate for multiple testing

Jean-Philippe Thiran, Dimitri Nestor Alice Van De Ville, Stephan Morgenthaler, Djalel Eddine Meskaldji

Over the last two decades, a large variety of type I error rates and control procedures have been proposed in the field of multiple hypotheses testing. This paper proposes a framework that includes many existing proposals by investigating procedures in whi ...

SPRINGER2020

Learning and Adaptive Data Analysis via Maximal Leakage

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

There has been growing interest in studying connections between generalization error of learning algorithms and information measures. In this work, we generalize a result that employs the maximal leakage, a measure of leakage of information, and explore ho ...

IEEE2019

Advantages of fluid and white matter suppression (FLAWS) with MP2RAGE compared with double inversion recovery turbo spin echo (DIR-TSE) at 7T

Tobias Kober

Cerebrospinal fluid (CSF) and white matter (WM) signal suppression techniques allow better visualization of both WM and gray matter (GM) lesions in such disorders as multiple sclerosis and epilepsy. Recently, a technique, FLuid And White matter Suppression ...

ELSEVIER IRELAND LTD2019

Statistical Applications of Random Matrix Theory: Comparison of Two Populations

Rémy Mariétan

During the last twenty years, Random matrix theory (RMT) has produced numerous results that allow a better understanding of large random matrices. These advances have enabled interesting applications in the domain of communication. Although this theory can ...

EPFL2019

Longitudinal analysis of white matter and cortical lesions in multiple sclerosis

Meritxell Bach Cuadra, Tobias Kober, Cristina Granziera, Mario Joao Fartaria de Oliveira

Purpose: The goals of this study were to assess the performance of a novel lesion segmentation tool for longitudinal analyses, as well as to validate the generated lesion progression map between two time points using conventional and non-conventional MR se ...

2019

On null hypotheses in survival analysis

Mats Julius Stensrud, Pal Christie Ryalen

The conventional nonparametric tests in survival analysis, such as the log-rank test, assess the null hypothesis that the hazards are equal at all times. However, hazards are hard to interpret causally, and other null hypotheses are more relevant in many s ...

2019

Topology classification with deep learning to improve real-time event selection at the LHC

Roberto Castello

We show how an event topology classification based on deep learning could be used to improve the purity of data samples selected in real-time at the Large Hadron Collider. We consider different data representations, on which different kinds of multi-class ...

2019

The detection of two-component mixture alternatives

Daria Rukina

In this thesis, we deal with one of the facets of the statistical detection problem. We study a particular type of alternative, the mixture model. We consider testing where the null hypothesis corresponds to the absence of a signal, represented by some kno ...

EPFL2018

Improved statistical evaluation of group differences in connectomes by screening-filtering strategy with application to study maturation of brain connections between childhood and adolescence

Jean-Philippe Thiran, Dimitri Nestor Alice Van De Ville, Stephan Morgenthaler, David Paul Roger Romascano, Patric Hagmann, Djalel Eddine Meskaldji

Detecting local differences between groups of connectomes is a great challenge in neuroimaging, because the large number of tests that have to be performed and the impact on multiplicity correction. Any available information should be exploited to increase ...

Elsevier2015

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