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Efficient model comparison techniques for models requiring large scale data augmentation

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Touloupou, Panayiota, Alzahrani, Naif, Neal, Peter, Spencer, Simon E. F. and McKinley, Trevelyan J. (2018) Efficient model comparison techniques for models requiring large scale data augmentation. Bayesian Analysis, 13 (2). pp. 437-459. doi:10.1214/17-BA1057

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Official URL: https://doi.org/10.1214/17-BA1057

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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
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH): Markov processes, Bayesian statistical decision theory, Monte Carlo method
Journal or Publication Title: Bayesian Analysis
Publisher: International Society for Bayesian Analysis
ISSN: 1931-6690
Official Date: 2018
Dates:
DateEvent
2018Published
29 July 2017Available
25 April 2017Accepted
Volume: 13
Number: 2
Page Range: pp. 437-459
DOI: 10.1214/17-BA1057
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
Funder: University of Warwick PhD studentship, Saudi Arabia. Wizārat al-Taʻlīm al-ʻĀlī

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