A comparison of power-expected-posterior priors in shrinkage regression

DOI10.1007/s42519-022-00284-6OpenAlexW4293788473MaRDI QIDQ2081738

Ioannis Ntzoufras, Dimitris Fouskakis, G. Tzoumerkas

Publication date: 30 September 2022

Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s42519-022-00284-6

zbMATH Keywords

MCMCshrinkage priorsBayesian variable selectionobjective priorsimaginary training samplesparse datasets

Mathematics Subject Classification ID

Linear inference, regression (62Jxx) Parametric inference (62Fxx) Statistical decision theory (62Cxx)

Cites Work

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• Power-expected-posterior priors for variable selection in Gaussian linear models
• Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
• Fully Bayes factors with a generalized \(g\)-prior
• Asymptotic properties of Bayes risk for the horseshoe prior
• Compatibility of prior specifications across linear models
• Prior distributions for objective Bayesian analysis
• Power-expected-posterior priors for generalized linear models
• Optimal predictive model selection.
• On the half-Cauchy prior for a global scale parameter
• Penalized regression, standard errors, and Bayesian Lassos
• The horseshoe estimator for sparse signals
• The Bayesian Lasso
• 10.1162/15324430152748236
• Expected-posterior prior distributions for model selection
• Bayesian Graphical Models for Discrete Data
• A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion