22 citations · 45 across the 10 of their papers we have counts for
4 papers · 1 filter
Spatiotemporally Consistent Multivariate Bias Correction for Climate Projections via Nested Vine Copulas
Theresa Meier, Erwan Koch, Valérie Chavez-Demoulin +1
Climate models are essential for understanding large-scale climate dynamics and long-term climate change, yet they exhibit systematic biases when compared with historical observati…
Throwing Vines at the Wall: Structure Learning via Random Search
Thibault Vatter, Thomas Nagler
Vine copulas offer flexible multivariate dependence modeling and have become widely used in machine learning. Yet, structure learning remains a key challenge. Early heuristics, suc…
Solving estimating equations with copulas
Thomas Nagler, Thibault Vatter
Thanks to their ability to capture complex dependence structures, copulas are frequently used to glue random variables into a joint model with arbitrary marginal distributions. Mor…
Generalized Additive Models for Pair-Copula Constructions
Thibault Vatter, Thomas Nagler
Pair-copula constructions are flexible dependence models that use bivariate copulas as building blocks. In this paper, we use generalized additive models to extend them by allowing…