6 citations · 9 across the 6 of their papers we have counts for
4 papers · 2 filters
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…
Interpretable Prediction Rule Ensembles in the Presence of Missing Data
Vincent Schroeder, Jakob Schwerter, Marjolein Fokkema +1
Prediction Rule Ensembles (PREs) are robust and interpretable statistical learning techniques with potential for predictive analytics, yet their efficacy in the presence of missing…
Evaluating tree-based imputation methods as an alternative to MICE PMM for drawing inference in empirical studies
Jakob Schwerter, Ketevan Gurtskaia, Andrés Romero +2
Dealing with missing data is an important problem in statistical analysis that is often addressed with imputation procedures. The performance and validity of such methods are of gr…
Adapting tree-based multiple imputation methods for multi-level data? A simulation study
Nico Föge, Jakob Schwerter, Ketevan Gurtskaia +2
When data have a hierarchical structure, such as students nested within classrooms, ignoring dependencies between observations can compromise the validity of imputation procedures.…