Sparse Regression under Correlation and Weak Signals: A Reproducible Benchmark of Classical and Bayesian Methods

This research benchmarks classical (Lasso, Ridge) and Bayesian (Horseshoe, Spike-and-Slab) sparse regression methods under challenging conditions like correlated features and weak signals. Bayesian methods generally outperform classical ones in prediction error, with Horseshoe offering excellent coverage, though classical methods are faster.