Workshop: Post-selection Inference and Multiple Testing
From Feb 7, 2018 (2 pm) to Feb 9, 2018 (12 noon).
The number and size of available data sets of different types has increased dramatically over the past twenty years. This increase has triggered a shift from hypothesis-driven research to data-driven research in many scientific areas. The recent fields of selective inference and post-selection inference aim at developing mathematically sound frameworks to provide confidence statements on findings that may have undergone selection effects (cherry-picking). This is particularly challenging in biomedical sciences and neuro-imaging, where the data are typically heterogeneous and complex.
The goal of this workshop is to discuss these challenges and present dedicated innovative approaches and their applications.
- Jean-Marc Azaïs (University of Toulouse, France)
- Yuval Benjamini (Hebrew University of Jerusalem, Israel)
- Andreas Buja (The Wharton School, University of Pennsylvania, USA)
- Jelle Goeman (Leiden University Medical Center, Netherlands)
- Ruth Heller (University of Tel-Aviv, Israel)
- Nikolaos Ignatiadis (Stanford University, USA)
- Matthieu Lerasle (Université Paris-Sud and CNRS, France)
- Amit Meir (University of Washington, USA)
- David Preinerstorfer (Université Libre de Bruxelles, Belgium)
- Aaditya Ramdas (University of California at Berkeley, USA)
- Etienne Roquain (Sorbonne Université, Paris, France)
- Aldo Solari (University of Milano-Bicocca, Italy)
post-selection inference -- selective inference -- multiple testing -- multi-scale data -- genomics -- neuro-imaging
Registration is now closed.
- Mélisande Albert (Institut de Mathématiques de Toulouse, INSA Toulouse, France)
- Gilles Blanchard (Universität Potsdam, Institut für Mathematik, Germany)
- Pierre Neuvial (Institut de Mathématiques de Toulouse, CNRS and University of Toulouse, France)
- Etienne Roquain (Laboratoire de Probabilités et Modèles Aléatoires, Université Pierre et Marie Curie, Paris, France)
Contact: Pierre Neuvial