Statistics for high dimensional data
V. Rivoirard

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 this talk is to present classical algorithms to deal with high-dimensional data such as penalized estimates with a special focus on Lasso-type estimators. We shall consider the classical regression setting but also generalized linear models and some possible extensions for functional data.