Mainstream statistical methodology is generally applicable to data observed in Euclidean
space. There are, however, numerous contexts of considerable scientific interest in which …

Goodness-of-fit tests based on the empirical Wasserstein distance are proposed for simple
and composite null hypotheses involving general multivariate distributions. For group …

In this article, the defining properties of any valid measure of the dependence between two
continuous random variables are revisited and complemented with two original ones, shown …

The capacities and the Choquet integral are powerful tools to represent decision problems
with dependencies and aggregate correlated decision criteria. Random generation of …

We explore the minimax optimality of goodness-of-fit tests on general domains using the
kernelized Stein discrepancy (KSD). The KSD framework offers a flexible approach for …

We consider goodness‐of‐fit tests for the multivariate Student's t‐distribution with iid data
and for the innovation distribution in a generalized autoregressive conditional …

We propose a class of goodness-of-fit tests for complete spatial randomness (CSR). In
contrast to standard tests, our procedure utilizes a transformation of the data to a binary …

We propose a general and relatively simple method to construct goodness-of-fit tests on the
sphere and the hypersphere. The method is based on the characterization of probability …

In sciences, the need often arises to test certain hypotheses and to create models, ie,
accurate and reliable simplifications of the world. To do this, one often has access to a …

Machine learning methods are getting more and more important in the development of
internal models using scenario generation. As internal models under Solvency 2 have to be …