3 citations · 6 across the 7 of their papers we have counts for
16 papers
Accounting for Time Dependency in Meta-Analyses of Concordance Probability Estimates
Matthias Schmid, Tim Friede, Nadja Klein +1
Recent years have seen the development of many novel scoring tools for disease prognosis and prediction. To become accepted for use in clinical applications, these tools have to be…
Distributional Adaptive Soft Regression Trees
Nikolaus Umlauf, Nadja Klein
Random forests are an ensemble method relevant for many problems, such as regression or classification. They are popular due to their good predictive performance (compared to, e.g.…
Boosting Distributional Copula Regression
Nicolai Hans, Nadja Klein, Florian Faschingbauer +2
Capturing complex dependence structures between outcome variables (e.g., study endpoints) is of high relevance in contemporary biomedical data problems and medical research. Distri…
Deselection of Base-Learners for Statistical Boosting -- with an Application to Distributional Regression
Annika Strömer, Christian Staerk, Nadja Klein +3
We present a new procedure for enhanced variable selection for component-wise gradient boosting. Statistical boosting is a computational approach that emerged from machine learning…
Marginally calibrated response distributions for end-to-end learning in autonomous driving
Clara Hoffmann, Nadja Klein
End-to-end learners for autonomous driving are deep neural networks that predict the instantaneous steering angle directly from images of the ahead-lying street. These learners mus…
A multivariate Gaussian random field prior against spatial confounding
Isa Marques, Thomas Kneib, Nadja Klein
Spatial models are used in a variety research areas, such as environmental sciences, epidemiology, or physics. A common phenomenon in many spatial regression models is spatial conf…