1 citations · 1 across the 4 of their papers we have counts for
5 papers
Prediction-based Variable Selection for Component-wise Gradient Boosting
Sophie Potts, Elisabeth Bergherr, Constantin Reinke +1
Model-based component-wise gradient boosting is a popular tool for data-driven variable selection. In order to improve its prediction and selection qualities even further, several…
Bayesian Boosting for Linear Mixed Models
Boyao Zhang, Colin Griesbach, Cora Kim +2
Boosting methods are widely used in statistical learning to deal with high-dimensional data due to their variable selection feature. However, those methods lack straightforward way…
Gradient Boosting for Linear Mixed Models
Colin Griesbach, Benjamin Säfken, Elisabeth Waldmann
Gradient boosting from the field of statistical learning is widely known as a powerful framework for estimation and selection of predictor effects in various regression models by a…
Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques
Colin Griesbach, Andreas Groll, Elisabeth Waldmann
Boosting techniques from the field of statistical learning have grown to be a popular tool for estimating and selecting predictor effects in various regression models and can rough…
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…