Spatial and spatio-temporal models with R-INLA.
By Marta Blangiardo, Michela Cameletti, Gianluca Baio, Håvard Rue. Spatial and Spatio-temporal Epidemiology, 2013
330 citations recorded by the source corpus.
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.
Subfields: Demography, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/3c6dc2cc4f38b57b1b0812f7e26e3979942955b8
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In Management Science and Operations Research
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Citations in the corpus
- Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation (2018, background): “extensions, such as spatial or temporal dependencies (e. g. Lindgren et al., 2011; Blangiardo et al., 2013) or measurement error in covariates (e.”