Bayesian model averaging in longitudinal studies using Bayesian variable selection methods

Abstract

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

Keywords:

• Bayesian modeling
• Bayesian variable selection
• Clustering
• Model selection
• Multimodal inference
• Information criteria

Acknowledgment

The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Hercules Foundation and the Flemish Government.

Disclosure statement

The authors declare that they have no conflict of interest.

Funding

Financial support from the Institutional University Cooperation of the Council of Flemish Universities (VLIR-IUC) is gratefully acknowledged.