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New Mean Field Approach for High-Dimensional Linear Regression

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Mean Field Approach for High-Dimensional Linear Regression

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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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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Sumit Mukherjee, Bodhisattva Sen, Subhabrata Sen ·

    A Mean Field Approach to Empirical Bayes Estimation in High-dimensional Linear Regression

    arXiv:2309.16843v3 Announce Type: replace-cross Abstract: We study empirical Bayes estimation in high-dimensional linear regression. To facilitate computationally efficient estimation of the underlying prior, we adopt a variational empirical Bayes approach, introduced originally …

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