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Proximal Empirical Bayes for Sparse Regression with Posterior Decision Support
Sparse regression requires both estimation and a decision about which effects to retain. Bayesian shrinkage supplies uncertainty for that decision, but richer prior hierarchies can make calibration and posterior computation demanding. We develop a computationally efficient empirical Bayes framework
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