A mixture of \(g\)-priors for variable selection when the number of regressors grows with the sample size

DOI10.1007/s11749-016-0516-0zbMath1382.62008arXiv1504.03796OpenAlexW2302374483MaRDI QIDQ1694372

Minerva Mukhopadhyay, Tapas Samanta

Publication date: 1 February 2018

Published in: Test (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1504.03796

zbMATH Keywords

Kullback-Leibler divergencelinear regressionmisspecified modelsmodel selection consistencygeneral class of distributions of errors

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Linear regression; mixed models (62J05) Bayesian inference (62F15)

Related Items (2)

Prior distributions for objective Bayesian analysis ⋮ On the correspondence from Bayesian log-linear modelling to logistic regression modelling with \(g\)-priors

Cites Work

• Consistency of Bayes factors under hyper \(g\)-priors with growing model size
• Bayes factor consistency for nested linear models with a growing number of parameters
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