High Dimensional Statistics, theory and practice

1st Oct 2017
High Dimensional Statistics, theory and practice
October 1-6, 2017, La Villa Clythia, Fréjus (France)

Classical statistical methods developed during the last century were suitable when the number of observations is much larger than the number of parameters to infer. Unfortunately, many fields such as astronomy, genetics, medicine or neuroscience produce large and complex data sets whose dimension is much larger than the number of experimental units. Such data are said to be high-dimensional. To face with this challenging curse of dimensionality, new methodologies have been developed based on sparsity assumptions.

The goal of these courses is to present the main concepts and ideas on some selected topics of high-dimensional statistics such as variable selection, nonparametric estimation, supervised and non-supervised classification, or multiple testing. Theoretical aspects are motivated by applicable developments of presented methods.


Details can be found at: Website.