NEWS: Workshop Post-selection inference and multiple testing in Toulouse, Feb. 7-9 2018

From classical inference...
... to post hoc inference

Rationale of the project

The number and size of available data sets of different types has increased dramatically over the past twenty years. This “data deluge” has been accompanied by a shift from hypothesis-driven research to data-driven research in many scientific fields including astronomy, biology, genetics, or medicine. Analyzing and interpreting such data require innovative approaches for the simultaneous testing of a large number of biological hypotheses.

This project gathers specialists of multiple testing theory, high-dimensional data analysis, and genomics. It aims at filling a gap between the statistical guarantees provided by state-of-the-art multiple testing procedures and the actual needs of practitioners.

We propose to develop “post hoc” procedures (in the sense of Goeman and Solari, Statistical Science, 2011), which provide confidence statements on the number or proportion of false positives among any subset of hypotheses chosen by the user after analyzing the data. Both theoretical and applied aspects of post hoc multiple testing will be covered.

Events

Preprints

  1. Blanchard G, Neuvial P, Roquain E: Post hoc inference via joint family-wise error rate control, 2017 [hal] [pdf] [url] [bib]
  2. Picard F, Reynaud-Bouret P, Roquain E: Continuous testing for Poisson process intensities: A new perspective on scanning statistics, 2017 [pdf] [url] [bib]
  3. Döhler S, Durand G, Roquain E: New procedures for discrete tests with proven false discovery rate control, 2017 [pdf] [url] [bib]

Participants

Toulouse  
Mélisande Albert INSA, Institut de Mathématiques de Toulouse
François Bachoc Université Paul Sabatier, Institut de Mathématiques de Toulouse
Maria Martinez INSERM UMR 1043
Pierre Neuvial CNRS, Institut de Mathématiques de Toulouse


Evry  
Cyril Dalmasso Université d’Evry, Laboratoire de Mathématiques et Modélisation d’Evry
Jean-François Deleuze Centre National de Génotypage
Edith Le Floch Centre National de Génotypage
Guillem Rigaill INRA, Laboratoire de Mathématiques et Modélisation d’Evry
Franck Samson INRA, Laboratoire de Mathématiques et Modélisation d’Evry


Paris  
Sylvain Delattre Université Paris 7, Laboratoire de Probabilités et Modèles Aléatoires
Guillermo Durand Université Paris 6, Laboratoire de Probabilités et Modèles Aléatoires
Etienne Roquain Université Paris 6, Laboratoire de Probabilités et Modèles Aléatoires


Potsdam  
Gilles Blanchard Universität Potsdam, Institut für Mathematik


Open source software

The R package sansSouci implements the methods developed in the course of the project.

Funding

Funded by ANR CNRS Labex CIMI

‘SansSouci’ has been identified as one of the best acronyms for ANR projects in 2016 by the Agence Nationale de l’Excellence Scientitique (ANES). See the official announcement on twitter.