1 citations · 3 across the 6 of their papers we have counts for
6 papers
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…
The zero degree of freedom non-central chi squared distribution for ensemble postprocessing
Jürgen Groß, Annette Möller
In this note the use of the zero degree non-central chi squared distribution as predictive distribution for ensemble postprocessing is investigated. It has a point mass at zero by…
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…
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…
Some Additional Remarks on Statistical Properties of Cohen's d from Linear Regression
Jürgen Groß, Annette Möller
The size of the effect of the difference in two groups with respect to a variable of interest may be estimated by the classical Cohen's . A recently proposed generalized estimat…
Effect Size Estimation in Linear Mixed Models
Jürgen Groß, Annette Möller
In this note, we reconsider Cohen's effect size measure under linear mixed models and demonstrate its application by employing an artificially generated data set. It is shown…