The SLOPE estimator has the particularity of having null components (sparsity) and
components that are equal in absolute value (clustering). The number of clusters depends …

SLOPE is a popular method for dimensionality reduction in the high-dimensional regression.
Indeed some regression coefficient estimates of SLOPE can be null (sparsity) or can be …

Abstract Sorted L-One Penalized Estimation (SLOPE) has shown the nice theoretical
property as well as empirical behavior recently on the false discovery rate (FDR) control of …

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 …

Popular regularizers with non-differentiable penalties, such as Lasso, Elastic Net,
Generalized Lasso, or SLOPE, reduce the dimension of the parameter space by inducing …

We concern new applications of discrete geometry and combinatorics in modern statistics.
First of them focuses on the use of penalized linear regresion methods. We start our …

The SLOPE estimator has the particularity of having null components (sparsity) and
components that are equal in absolute value (clustering). The number of clusters depends …