Informative g -Priors for Mixed Models

[摘要] Zellner’s objective g-prior has been widely used in linear regression models due to its simple interpretation and computational tractability in evaluating marginal likelihoods. However, the g-prior further allows portioning the prior variability explained by the linear predictor versus that of pure noise. In this paper, we propose a novel yet remarkably simple g-prior specification when a subject matter expert has information on the marginal distribution of the response y i. The approach is extended for use in mixed models with some surprising but intuitive results. Simulation studies are conducted to compare the model fitting under the proposed g-prior with that under other existing priors.

[学科分类] 农艺学与作物科学

[关键词] prior elicitation;g-priors;linear regression;Bayesian model selection;mixed models;variable selection;Bayes factor