Graphical SLOPE achieves root-n consistent precision matrix estimation with asymptotic edge clustering, and TSLOPE reduces variability under elliptical heavy-tailed distributions compared to Gaussian-loss versions.
Unveiling low- dimensional patterns induced by convex non-differentiable regularizers.Annals of the Institute of Statistical Mathematics, pages 1–36
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Asymptotic Theory for Graphical SLOPE: Precision Estimation and Pattern Convergence
Graphical SLOPE achieves root-n consistent precision matrix estimation with asymptotic edge clustering, and TSLOPE reduces variability under elliptical heavy-tailed distributions compared to Gaussian-loss versions.