376 citations · 641 across the 11 of their papers we have counts for
15 papers
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…
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…
Estimating the course of the COVID-19 pandemic in Germany via spline-based hierarchical modelling of death counts
Tobias Wistuba, Andreas Mayr, Christian Staerk
The effective reproduction number is a key figure to monitor the course of the COVID-19 pandemic. In this study we consider a retrospective modelling approach for estimating the ef…
Extension of the Gradient Boosting Algorithm for Joint Modeling of Longitudinal and Time-to-Event data
Colin Griesbach, Andreas Mayr, Elisabeth Waldmann
In various data situations joint models are an efficient tool to analyze relationships between time dependent covariates and event times or to correct for event-dependent dropout o…
Gradient boosting in Markov-switching generalized additive models for location, scale and shape
Timo Adam, Andreas Mayr, Thomas Kneib
We propose a novel class of flexible latent-state time series regression models which we call Markov-switching generalized additive models for location, scale and shape. In contras…