New Mean Field Approach for High-Dimensional Linear Regression

This paper introduces a novel mean field approach for empirical Bayes estimation in high-dimensional linear regression. The method utilizes a variational empirical Bayes technique to efficiently estimate the underlying prior, establishing asymptotic consistency for the nonparametric maximum likelihood estimator and its mean field variational surrogate. The research also develops a computationally feasible approximation to the oracle posterior distribution, enabling accurate Bayesian inference, including the construction of credible intervals and Bayes optimal estimation for regression coefficients. AI

IMPACT Introduces a new statistical method for high-dimensional linear regression, potentially improving model accuracy and inference capabilities in AI applications.

RANK_REASON The cluster contains an academic paper detailing a new statistical method. [lever_c_demoted from research: ic=1 ai=0.7]

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• Carbonetto and Stephens
• Subhabrata Sen

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