4 papers
Why is Regularization Underused? An Empirical Study on Trust and Adoption of Statistical Methods
Konstantin Emil Thiel, Marléne Baumeister, Nicole Krämer +3
Statistical practice does not automatically follow methodological innovation. Regularization methods, widely advocated to reduce overfitting and stabilize inference, are readily av…
Variable selection in linear mixed model meta-regression with suspected interaction effects -- How can tree-based methods help?
Jan-Bernd Igelmann, Paula Lorenz, Markus Pauly
Detecting interaction effects (IEs) in meta-regression is challenging, especially when few studies are available and many plausible interactions are considered. In many meta-analys…
Confidence Intervals for Random Forest Permutation Importance with Missing Data
Nico Föge, Markus Pauly
Random Forests are renowned for their predictive accuracy, but valid inference, particularly about permutation-based feature importances, remains challenging. Existing methods, suc…
Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Study
Jakob Schwerter, Andrés Romero, Florian Dumpert +1
Tree-based learning methods such as Random Forest and XGBoost are still the gold-standard prediction methods for tabular data. Feature importance measures are usually considered fo…