3 citations · 6 across the 8 of their papers we have counts for
14 papers · 1 filter
Copulas for Geostatistical Data: Foundations, Modeling Principles and Statistical Inference
Axel Bücher, Nadja Klein
Spatial statistics commonly describes spatial dependence through second-order quantities such as covariance functions and variograms, often within Gaussian random-field models and…
Truly Multivariate Structured Additive Distributional Regression
Lucas Kock, Nadja Klein
Generalized additive models for location, scale and shape (GAMLSS) are a popular extension to mean regression models where each parameter of an arbitrary distribution is modelled t…
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…