Bi-level variable selection with the sparse group penalty framework

Abstract

Introduction: Bi-level selection methods account for grouped predictors in the selection process to identify relevant variable groups and highlight their predictive members. This property is particularly helpful when analyzing omics datasets, as such data is often characterized by a natural group structure [for full text, please go to the a.m. URL]