Robust Regularised M-Estimators for High-Dimensional Regression with Heavy-Tailed and Skewed Errors
This paper introduces a class of robust regularised M-estimators for high-dimensional regression that achieves optimal convergence rates and variable selection consistency under minimal moment conditions (finite ), outperforming existing methods in both simulation and real-world genomic applications when dealing with heavy-tailed, skewed, or contaminated errors.