1 citations · 1 across the 7 of their papers we have counts for
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Properties of stepwise parameter estimation in high-dimensional vine copulas
Jana Gauss, Thomas Nagler
The increasing use of vine copulas in high-dimensional settings, where the number of parameters is often of the same order as the sample size, calls for asymptotic theory beyond th…
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…
Mortality Forecasting under Climate Risk: A Stochastic Approach with Distributed Lag Non-Linear Models
Jiacheng Min, Han Li, Thomas Nagler +1
Assessing climate-driven mortality risk has become an emerging area of research in recent decades. In this paper, we propose a novel approach to explicitly incorporate climate-driv…
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…