The Strong Screening Rule For SLOPE
Extracting relevant features from data sets where the number of observations n is much smaller then the number of predictors p is a major challenge in modern statistics. Sorted L-One Penalized Estimation (SLOPE)—a generalization of the lasso---is a promising method within this setting. Current numerical procedures for SLOPE, however, lack the efficiency that respective tools for the lasso enjoy, p