Efficient model comparison techniques for models requiring large scale data augmentation

Touloupou, Panayiota and Alzahrani, Naif and Neal, Peter John and Spencer, Simon and McKinley, Trevelyan (2018) Efficient model comparison techniques for models requiring large scale data augmentation. Bayesian Analysis, 13 (2). pp. 437-459. ISSN 1936-0975

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Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
Bayesian Analysis
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2604
Subjects:
ID Code:
86037
Deposited By:
Deposited On:
26 Apr 2017 12:44
Refereed?:
Yes
Published?:
Published
Last Modified:
22 Oct 2020 04:19