4 papers
Model Selection for SLOPE Models: A Bayesian Perspective
Fabio Feser, Marina Evangelou
Sorted Penalized Estimation (SLOPE) models, that perform either variable or group selection, control the false discovery rate (FDR) under orthogonal settings with known no…
Dual Feature Reduction for the Sparse-group Lasso and its Adaptive Variant
Fabio Feser, Marina Evangelou
The sparse-group lasso performs both variable and group selection, simultaneously using the strengths of the lasso and group lasso. It has found widespread use in genetics, a field…
Strong Screening Rules for Group-based SLOPE Models
Fabio Feser, Marina Evangelou
Tuning the regularization parameter in penalized regression models is an expensive task, requiring multiple models to be fit along a path of parameters. Strong screening rules dras…
Sparse-group SLOPE: adaptive bi-level selection with FDR-control
Fabio Feser, Marina Evangelou
In this manuscript, a new high-dimensional approach for simultaneous variable and group selection is proposed, called sparse-group SLOPE (SGS). SGS achieves false discovery rate co…