Bayesian model averaging in longitudinal studies using Bayesian variable selection methods

Parameter estimation is often considered as a post model selection problem, i.e., the parameters of interest are estimated based on “the best” model. However, this approach does not take into account that was selected from set possible models. Ignoring uncertainty may lead to bias in estimation. In paper, we present Bayesian variable (BVS) for averaging which would address uncertainty. Although be preferred approach, BVS can used well if select one among candidate The performance compared with information criterion real longitudinal data and through simulations study.