David Kaplan - (Department of Educational Psychology, University of Wisconsin - Madison)
Abstract
This talk presents results on the use of Bayesian model averaging as a means of addressing predictive performance in Bayesian structural equation models. The current approach to addressing the problem of model uncertainty from a Bayesian perspective lies in the method of Bayesian model averaging. We expand the work of Madigan and his colleagues by considering a structural equation model as a special case of a directed acyclic graph. We then provide an algorithm that searches the model space for sub-models and obtains a weighted average of the sub-models using posterior model probabilities as weights. Our simulation study provides a frequentist evaluation of our Bayesian model averaging approach and indicates that when the true model is known, Bayesian model averaging does not yield necessarily better predictive performance compared to non-averaged models. However, our case study using data from an international large-scale assessment reveals that the model-averaged sub-models provide better posterior predictive performance compared to the initially specified model.
Contact person
Silvia Bianconcini