242 citations · 778 across the 62 of their papers we have counts for
4 papers · 1 filter
Risk bounds when learning infinitely many response functions by ordinary linear regression
Vincent Plassier, François Portier, Johan Segers
Consider the problem of learning a large number of response functions simultaneously based on the same input variables. The training data consist of a single independent random sam…
Multivariate goodness-of-Fit tests based on Wasserstein distance
Marc Hallin, Gilles Mordant, Johan Segers
Goodness-of-fit tests based on the empirical Wasserstein distance are proposed for simple and composite null hypotheses involving general multivariate distributions. For group fami…
Empirical tail copulas for functional data
John H. J. Einmahl, Johan Segers
For multivariate distributions in the domain of attraction of a max-stable distribution, the tail copula and the stable tail dependence function are equivalent ways to capture the…
Inference on extremal dependence in the domain of attraction of a structured Hüsler-Reiss distribution motivated by a Markov tree with latent variables
Stefka Asenova, Gildas Mazo, Johan Segers
A Markov tree is a probabilistic graphical model for a random vector indexed by the nodes of an undirected tree encoding conditional independence relations between variables. One p…