Bayes factor consistency for nested linear models with a growing number of parameters

DOI10.1016/j.jspi.2013.11.001zbMath1432.62216OpenAlexW2008314096WikidataQ58045668 ScholiaQ58045668MaRDI QIDQ389299

Xiaoqian Sun, Min Wang

Publication date: 20 January 2014

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2013.11.001

zbMATH Keywords

Bayes factorZellner's \(g\)-priormodel selection consistencybeta-prime priorgrowing number of parameters

Mathematics Subject Classification ID

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

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, An exact approach to ridge regression for big data, A mixture of \(g\)-priors for variable selection when the number of regressors grows with the sample size, Bayesian bandwidth test and selection for high-dimensional banded precision matrices, Consistency of Bayes factor for nonnested model selection when the model dimension grows, Consistency of Bayes factors under hyper \(g\)-priors with growing model size

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Fully Bayes factors with a generalized \(g\)-prior
• Intrinsic priors for model comparison in multivariate normal regression
• Consistency of objective Bayes factors for nonnested linear models and increasing model dimension
• Consistency of objective Bayes factors as the model dimension grows
• Consistency of Bayesian procedures for variable selection
• Comparison of Bayesian objective procedures for variable selection in linear regression
• Default Bayesian analysis of the Behrens-Fisher problem
• Approximations and consistency of Bayes factors as model dimension grows
• Empirical Bayes vs. fully Bayes variable selection
• Calibration and empirical Bayes variable selection
• Bayes factor consistency for unbalanced ANOVA models
• A comment on D. V. Lindley's statistical paradox
• Mixtures of g Priors for Bayesian Variable Selection
• A Bayesian approach to inference about a change-point in a sequence of random variables
• Bayes Factors
• Objective Testing Procedures in Linear Models: Calibration of the p‐values
• Objective Bayesian Variable Selection
• Benchmark priors for Bayesian model averaging.