On consistency and optimality of Bayesian variable selection based on \(g\)-prior in normal linear regression models
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Publication:498061
DOI10.1007/s10463-014-0483-8zbMath1440.62277OpenAlexW2027620321MaRDI QIDQ498061
Minerva Mukhopadhyay, Tapas Samanta, Arijit Chakrabarti
Publication date: 25 September 2015
Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10463-014-0483-8
Linear regression; mixed models (62J05) Bayesian inference (62F15) Bayesian problems; characterization of Bayes procedures (62C10)
Related Items
Bayes factor asymptotics for variable selection in the Gaussian process framework, Posterior consistency of \(g\)-prior for variable selection with a growing number of parameters, Consistency of Bayes factors under hyper \(g\)-priors with growing model size
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