Computational Statistics

Extracting information from complex data, with huge number of features (high dimensional settings) is currently one of the most important challenge for data analytics. On the one side, new statistical methods, valid in high dimensions (conceptually, dimensions that can be infinite), need to be developed and, on the other side, these methods should be computable in practice within the limits of available computer performances. We contribute to the development of computationally efficient statistical methods for estimation, inference and model selection in high dimensions, with applications to medical sciences.