Comparison of Bayesian objective procedures for variable selection in linear regression

DOI10.1007/s11749-006-0039-1zbMath1367.62070OpenAlexW4230313596MaRDI QIDQ1019478

Elías Moreno, Fco. Javier Girón

Publication date: 2 June 2009

Published in: Test (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10.1007/s11749-008-0095-9

zbMATH Keywords

model selectionlinear regressionintrinsic priorsreference priorsencompassing

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Bayesian inference (62F15)

Related Items

Power-expected-posterior priors for variable selection in Gaussian linear models, Bayes factor asymptotics for variable selection in the Gaussian process framework, Prior distributions for objective Bayesian analysis, Power-expected-posterior priors for generalized linear models, Objective Bayesian group variable selection for linear model, Bayes factor consistency for nested linear models with a growing number of parameters, Computation for intrinsic variable selection in normal regression models via expected-posterior prior, Information consistency of the Jeffreys power-expected-posterior prior in Gaussian linear models, Power-expected-posterior priors as mixtures of \(g\)-priors in normal linear models, Compatibility of prior specifications across linear models, Bayes Factor Consistency for One-way Random Effects Model, Consistency of Bayes factor for nonnested model selection when the model dimension grows, Objective Bayesian variable selection in linear regression model, Bayes factor consistency for unbalanced ANOVA models, Posterior model consistency in variable selection as the model dimension grows

Uses Software

• ElemStatLearn

Cites Work

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