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August 5, 2025

Parameter Expanded Variational Bayes for Well-Calibrated High-Dimensional Linear Regression with Spike-and-Slab Priors

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Authors

POPeter OlejuaUniversity of South CarolinaESEnakshi SahaUniversity of South CarolinaRGRahul GhosalUniversity of South Carolina

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Overview

Parameter expanded variational bayes improves predictive calibration in high-dimensional regression, suggesting enhanced robustness to prior specifications.

Key Points

  • MAIN FINDING: The proposed sparse parameter-expanded variational bayes approach offers enhanced predictive calibration in high-dimensional regression.
  • KEY EVIDENCE: spexvb consistently shows lower predictive error and improved variable selection accuracy compared to standard variational bayes.
  • APPROACH: The method applies parameter expansion to variational bayes under the mean-field assumption for better forecast accuracy.
  • SIGNIFICANCE: Improved robustness to prior settings enhances the reliability of predictions in complex statistical models.

Cite This Study

Olejua et al. (2025) studied this question.

synapsesocial.com/papers/689a0f93e6551bb0af8d12ddhttps://doi.org/10.21203/rs.3.rs-7208847/v1
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Also Consider

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  4. 4Robust Sparse Identification of Linear Parameter‐Varying Systems Using Variational Bayesian With Spike‐and‐Slab Prior2026
  5. 5Loss-Based Variational Bayes Prediction2024 · 3 citations