A Bayes factor with reasonable model selection consistency for ANOVA model

Abstract

For the ANOVA model, we propose a new g-prior based Bayes factor without integral representation, with reasonable model selection consistency for any asymptotic situations (either number of levels of the factor and/or number of replication in each level goes to infinity). Exact analytic calculation of the marginal density under a special choice of the priors enables such a Bayes factor.

a major revision

References in corpus (2)

• Consistency of objective Bayes factors as the model dimension grows
• A new class of generalized Bayes minimax ridge regression estimators

Cited by in corpus (4)

• The Pearson Bayes factor: An analytic formula for computing evidential value from minimal summary statistics
• Consistency of Bayes factor for nonnested model selection when the model dimension grows
• Bayes Factor Consistency for One-way Random Effects Model
• Bayes Factor Consistency for Unbalanced ANOVA Models