3 papers
stat.ME2021
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…
stat.ME2021
Adaptive Step-Length Selection in Gradient Boosting for Generalized Additive Models for Location, Scale and Shape
Boyao Zhang, Tobias Hepp, Sonja Greven +1
Tuning of model-based boosting algorithms relies mainly on the number of iterations, while the step-length is fixed at a predefined value. For complex models with several predictor…
stat.ME2019
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…