5 papers
Asymptotic Theory for Graphical SLOPE: Precision Estimation and Pattern Convergence
Ivan Hejný, Giovanni Bonaccolto, Philipp Kremer +3
This paper studies Graphical SLOPE for precision matrix estimation, with emphasis on its ability to recover both sparsity and clusters of edges with equal or similar strength. In a…
Identifying Network Hubs with the Partial Correlation Graphical LASSO
Małgorzata Bogdan, Adam Chojecki, Ivan Hejný +2
Graphical LASSO (GLASSO) is a widely used method for estimating sparse precision matrices and learning undirected graphical models in high-dimensional settings. Because GLASSO pena…
Asymptotic Distribution of Low-Dimensional Patterns Induced by Non-Differentiable Regularizers under General Loss Functions
Ivan Hejný, Jonas Wallin, Małgorzata Bogdan
This article investigates the asymptotic distribution of penalized estimators with non-differentiable penalties designed to recover low-dimensional pattern structures. Patterns pla…
Unveiling low-dimensional patterns induced by convex non-differentiable regularizers
Ivan Hejný, Jonas Wallin, Małgorzata Bogdan +1
Popular regularizers with non-differentiable penalties, such as Lasso, Elastic Net, Generalized Lasso, or SLOPE, reduce the dimension of the parameter space by inducing sparsity or…
Weak pattern convergence for SLOPE and its robust versions
Ivan Hejný, Jonas Wallin, Małgorzata Bogdan
The Sorted L-One Estimator (SLOPE) is a popular regularization method in regression, which induces clustering of the estimated coefficients. That is, the estimator can have coeffic…