Bayesian methods outperform classical sparse regression in prediction and uncertainty

A new benchmark study evaluated six sparse regression methods, comparing classical approaches like Lasso with Bayesian techniques such as Horseshoe and Spike-and-Slab. The research found that Bayesian methods generally offered superior prediction error and more accurate uncertainty estimates, with the Horseshoe prior achieving near-nominal coverage. However, for variable selection, Lasso and Spike-and-Slab performed comparably, suggesting Lasso remains a practical choice when full posterior estimates are not required. AI

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IMPACT Provides a comparative analysis of regression techniques, informing practitioners on method selection for prediction and variable selection under challenging data conditions.

RANK_REASON This is a benchmark study of classical and Bayesian methods published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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• Lasso
• Horseshoe
• Spike-and-Slab
• Diabetes dataset
• OLS
• Ridge
• Elastic Net

• paper
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