1 citations · 1 across the 3 of their papers we have counts for
3 papers
stat.ME2024
Gradient-Boosted Generalized Linear Models for Conditional Vine Copulas
David Jobst, Annette Möller, Jürgen Groß
Vine copulas are flexible dependence models using bivariate copulas as building blocks. If the parameters of the bivariate copulas in the vine copula depend on covariates, one obta…
stat.AP2024
Time Series based Ensemble Model Output Statistics for Temperature Forecasts Postprocessing
David Jobst, Annette Möller, Jürgen Groß
Nowadays, weather prediction is based on numerical weather prediction (NWP) models to produce an ensemble of forecasts. Despite of large improvements over the last few decades, the…
stat.ME2023★ 1 cited
D-Vine GAM Copula based Quantile Regression with Application to Ensemble Postprocessing
David Jobst, Annette Möller, Jürgen Groß
Temporal, spatial or spatio-temporal probabilistic models are frequently used for weather forecasting. The D-vine (drawable vine) copula quantile regression (DVQR) is a powerful to…