Robust Bayesian high-dimensional variable selection and inference with the horseshoe family of priors

Key Points

• Robust bayesian inference enhances variable selection in high-dimensional settings, leading to more reliable insights.
• The proposed method demonstrates effectiveness across various scenarios, achieving significant improvements in metrics like accuracy.
• Utilizing horseshoe family priors, the analysis offers a flexible approach, accommodating complex data structures and dependencies.
• This approach may enable more precise modeling in fields that rely on high-dimensional data, though further validation is needed.