Blangiardo, M;
Cameletti, M;
Baio, G;
Rue, H;
(2013)
Spatial and spatio-temporal models with R-INLA.
Spatial and Spatio-temporal Epidemiology
, 7
39 - 55.
10.1016/j.sste.2013.07.003.
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Abstract
During the last three decades, Bayesian methods have developed greatly in the field of epidemiology. Their main challenge focusses around computation, but the advent of Markov Chain Monte Carlo methods (MCMC) and in particular of the WinBUGS software has opened the doors of Bayesian modelling to the wide research community. However model complexity and database dimension still remain a constraint.Recently the use of Gaussian random fields has become increasingly popular in epidemiology as very often epidemiological data are characterised by a spatial and/or temporal structure which needs to be taken into account in the inferential process. The Integrated Nested Laplace Approximation (INLA) approach has been developed as a computationally efficient alternative to MCMC and the availability of an R package (R-INLA) allows researchers to easily apply this method.In this paper we review the INLA approach and present some applications on spatial and spatio-temporal data. © 2012 Elsevier Ltd.
| Type: | Article |
|---|---|
| Title: | Spatial and spatio-temporal models with R-INLA |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.sste.2013.07.003 |
| Publisher version: | http://dx.doi.org/10.1016/j.sste.2013.07.003 |
| Language: | English |
| Additional information: | © 2013. This manuscript version is published under a Creative Commons Attribution Non-commercial Non-derivative 4.0 International licence (CC BY-NC-ND 4.0). This licence allows you to share, copy, distribute and transmit the work for personal and non-commercial use providing author and publisher attribution is clearly stated. Further details about CC BY licences are available at http://creativecommons.org/licenses/by/4.0. Please note that the erratum referred to in the published version of the article does not apply to this manuscript version, as it was necessitated by the publisher introducing formatting errors during typesetting. |
| Keywords: | Integrated Nested Laplace Approximation; Stochastic Partial Differential Equation approach; Bayesian approach; Area-level data; Point-level data |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/1415919 |
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