  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 modifications of the original
algorithm have been developed, that mainly focus on different stopping
criteria, leaving the actual variable selection mechanism untouched. We
investigate different prediction-based mechanisms for the variable selection
step in model-based component-wise gradient boosting. These approaches include
Akaikes Information Criterion (AIC) as well as a selection rule relying on the
component-wise test error computed via cross-validation. We implemented the AIC
and cross-validation routines for Generalized Linear Models and evaluated them
regarding their variable selection properties and predictive performance. An
extensive simulation study revealed improved selection properties whereas the
prediction error could be lowered in a real world application with
age-standardized COVID-19 incidence rates.
