Effect of model space priors on statistical inference with model uncertainty.

ABSTRACT: Bayesian model averaging (BMA) provides a coherent way to account for model uncertainty in statistical inference tasks. BMA requires specification of model space priors and parameter space priors. In this article we focus on comparing different model space priors in presence of model uncertainty. We consider eight reference model space priors used in the literature and three adaptive parameter priors recommended by Porwal and Raftery [37]. We assess the performance of these combinations of prior specifications for variable selection in linear regression models for the statistical tasks of parameter estimation, interval estimation, inference, point and interval prediction. We carry out an extensive simulation study based on 14 real datasets representing a range of situations encountered in

SUBMITTER: Porwal A 

PROVIDER: S-EPMC11482600 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature