Efficient Model Comparison Techniques for Models Requiring Large Scale Data Augmentation

This is the final version of the article. Available from ISBA via the DOI in this record.Selecting between competing statistical models is a challenging problem especially when the competing models are non-nested. In this paper we offer a simple solution by devising an algorithm which combines MCMC and importance sampling to obtain computationally efficient estimates of the marginal likelihood which can then be used to compare the models. The algorithm is successfully applied to a longitudinal epidemic data set, where calculating the marginal likelihood is made more challenging by the presence of large amounts of missing data. In this context, our importance sampling approach is shown to outperform existing methods for computing the marginal likelihood.PT was supported by a University of Warwick PhD scholarship. NA was supported by a PhD scholarship from the Saudi Arabian Government