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On the use of marginal posteriors in marginal likelihood estimation via importance sampling

Perrakis, Konstantinos; Ntzoufras, Ioannis; Tsionas, Efthymios G.

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Authors

Ioannis Ntzoufras

Efthymios G. Tsionas



Abstract

The efficiency of a marginal likelihood estimator where the product of the marginal posterior distributions is used as an importance sampling function is investigated. The approach is generally applicable to multi-block parameter vector settings, does not require additional Markov Chain Monte Carlo (MCMC) sampling and is not dependent on the type of MCMC scheme used to sample from the posterior. The proposed approach is applied to normal regression models, finite normal mixtures and longitudinal Poisson models, and leads to accurate marginal likelihood estimates.

Citation

Perrakis, K., Ntzoufras, I., & Tsionas, E. G. (2014). On the use of marginal posteriors in marginal likelihood estimation via importance sampling. Computational Statistics & Data Analysis, 77, 54-69. https://doi.org/10.1016/j.csda.2014.03.004

Journal Article Type Article
Acceptance Date Mar 8, 2014
Online Publication Date Mar 19, 2014
Publication Date Sep 1, 2014
Deposit Date Sep 26, 2019
Publicly Available Date Oct 8, 2019
Journal Computational Statistics & Data Analysis
Print ISSN 0167-9473
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 77
Pages 54-69
DOI https://doi.org/10.1016/j.csda.2014.03.004
Related Public URLs https://arxiv.org/pdf/1311.0674.pdf

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