Consistency of Bayes factor for nonnested model selection when the model dimension grows

DOI10.3150/15-BEJ720zbMath1358.62033arXiv1503.06155WikidataQ58045607 ScholiaQ58045607MaRDI QIDQ726729

Min Wang, Yuzo Maruyama

Publication date: 14 July 2016

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1503.06155

zbMATH Keywords

Bayes factormodel selection consistencygrowing number of parametersnonnested linear models

Mathematics Subject Classification ID

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

Related Items (5)

Bayesian and frequentist evidence in one-sided hypothesis testing ⋮ 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 ⋮ Bayesian bandwidth test and selection for high-dimensional banded precision matrices ⋮ Efficient Functional ANOVA Through Wavelet-Domain Markov Groves

Cites Work

• Posterior model consistency in variable selection as the model dimension grows
• Bayes factor consistency for nested linear models with a growing number of parameters
• Mixtures of \(g\)-priors for Bayesian model averaging with economic applications
• Fully Bayes factors with a generalized \(g\)-prior
• Criteria for Bayesian model choice with application to variable selection
• Bayesian structured variable selection in linear regression models
• 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
• Two-way ANOVA models with unbalanced data
• Approximations and consistency of Bayes factors as model dimension grows
• Bayes factor consistency for unbalanced ANOVA models
• Conservative prior distributions for variance parameters in hierarchical models
• Mixtures of g Priors for Bayesian Variable Selection
• NONNESTED LINEAR MODEL SELECTION REVISITED
• Bayes Factors
• Objective Testing Procedures in Linear Models: Calibration of the p‐values
• On the Choice of Forecasting Formulas
• Benchmark priors for Bayesian model averaging.
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