138 citations · 302 across the 32 of their papers we have counts for
6 papers · 1 filter
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 Multivariate Structured Additive Distributional Regression Models
Annika Strömer, Nadja Klein, Christian Staerk +2
We develop a model-based boosting approach for multivariate distributional regression within the framework of generalized additive models for location, scale, and shape. Our approa…
Actor Heterogeneity and Explained Variance in Network Models -- A Scalable Approach through Variational Approximations
Nadja Klein, Göran Kauermann
The analysis of network data has gained considerable interest in recent years. This also includes the analysis of large, high-dimensional networks with hundreds and thousands of no…
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…