11 citations · 13 across the 7 of their papers we have counts for
8 papers
Prescreen, Then Subsample: Efficient model training for large-scale biomedical data with correlated covariates
Subhadra Dasgupta, Christian Staerk
Statistical modeling of high-dimensional and large-sample data with complex correlation structures is increasingly important in biomedical research, particularly in large-scale omi…
Enhanced variable selection for boosting sparser and less complex models in distributional copula regression
Annika Strömer, Nadja Klein, Christian Staerk +3
Structured additive distributional copula regression allows to model the joint distribution of multivariate outcomes by relating all distribution parameters to covariates. Estimati…
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…
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…
A Metropolized adaptive subspace algorithm for high-dimensional Bayesian variable selection
Christian Staerk, Maria Kateri, Ioannis Ntzoufras
A simple and efficient adaptive Markov Chain Monte Carlo (MCMC) method, called the Metropolized Adaptive Subspace (MAdaSub) algorithm, is proposed for sampling from high-dimensiona…
Estimating effective infection fatality rates during the course of the COVID-19 pandemic in Germany
Christian Staerk, Tobias Wistuba, Andreas Mayr
The infection fatality rate (IFR) of the Coronavirus Disease 2019 (COVID-19) is one of the most discussed figures in the context of this pandemic. Using German COVID-19 surveillanc…
High-dimensional variable selection via low-dimensional adaptive learning
Christian Staerk, Maria Kateri, Ioannis Ntzoufras
A stochastic search method, the so-called Adaptive Subspace (AdaSub) method, is proposed for variable selection in high-dimensional linear regression models. The method aims at fin…