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stat.ME2025
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…
stat.ME2025
On dimension reduction in conditional dependence models
Thomas Nagler, Gerda Claeskens, Irène Gijbels
Inference of the conditional dependence structure is challenging when many covariates are present. In numerous applications, only a low-dimensional projection of the covariates inf…
stat.ME2024
Simplified vine copula models: state of science and affairs
Thomas Nagler
Vine copula models have become highly popular practical tools for modeling multivariate dependencies. To maintain tractability, a commonly employed simplifying assumption is that c…