Gregor Buch, Andreas Schulz, Irene Schmidtmann, Konstantin Strauch, Philipp S Wild
Variable selection is usually performed to increase interpretability, as sparser models are easier to understand than full models. However, a focus on sparsity is not always suitable, for example, when features are related due to contextual similarities or high correlations. Here, it may be more appropriate to identify groups and their predictive members, a task that can be accomplished with bi-level selection procedures. To investigate whether such techniques lead to increased interpretability, group exponential LASSO (GEL), sparse group LASSO (SGL), composite minimax concave penalty (cMCP), and least absolute shrinkage, and selection operator (LASSO) as reference methods were used to select predictors in time-to-event, regression, and classification tasks in bootstrap samples from a cohort of 1001 patients...
March 2024: Biometrical Journal. Biometrische Zeitschrift