1 citations · 1 across the 7 of their papers we have counts for
9 papers
Optimal neural network approximation of smooth compositional functions on sets with low intrinsic dimension
Thomas Nagler, Sophie Langer
We study approximation and statistical learning properties of deep ReLU networks under structural assumptions that mitigate the curse of dimensionality. We prove minimax-optimal un…
Fast Rates for Nonstationary Weighted Risk Minimization
Tobias Brock, Thomas Nagler
Weighted empirical risk minimization is a common approach to prediction under distribution drift. This article studies its out-of-sample prediction error under nonstationarity. We…
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…
Asymptotics for estimating a diverging number of parameters -- with and without sparsity
Jana Gauss, Thomas Nagler
We develop a general asymptotic theory for estimating equations whose dimension diverges with the sample size. For both unpenalized and sparse penalized problems, we establish popu…
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…
Towards more realistic climate model outputs: A multivariate bias correction based on zero-inflated vine copulas
Henri Funk, Ralf Ludwig, Helmut Kuechenhoff +1
Climate model large ensembles are an essential research tool for analysing and quantifying natural climate variability and providing robust information for rare extreme events. The…