Robust Statistics

Data analysis is part of the scientific process, as it can be used to validate/invalidate scientific hypotheses and/or to predict expected outcomes given a model. The later can be more or less flexible and/or general, but is always based on a (minimal) set of assumptions. With the availability of huge amounts of data, deviations from these assumptions (model deviations), such as so-called outliers, often occur in observed data sets. Robust statistics provides a theoretical framework for estimation and inference techniques that are less sensitive to (any type of) model deviations. We contribute to the development of robust methods for time series and spatial statistics analysis, model selection and (flexible) multivariate modelling (copulas), with applications to economics, behavioral sciences, signal processing.