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dc.contributor.authorBivand, Roger
dc.contributor.authorGómez-Rubio, Virgilio
dc.contributor.authorRue, Håvard
dc.identifier.citationJournal of Statistical Software 2015, 63(20)nb_NO
dc.description.abstractThe integrated nested Laplace approximation (INLA) provides an interesting way of approximating the posterior marginals of a wide range of Bayesian hierarchical models. This approximation is based on conducting a Laplace approximation of certain functions and numerical integration is extensively used to integrate some of the models parameters out. The R - INLA package o ers an interface to INLA, providing a suitable framework for data analysis. Although the INLA methodology can deal with a large number of models, only the most relevant have been implemented within R - INLA . However, many other important models are not available for R - INLA yet. In this paper we show how to fit a number of spatial models with R - INLA , including its interaction with other R packages for data analysis. Secondly, we describe a novel method to extend the number of latent models available for the model parameters. Our approach is based on conditioning on one or several model parameters and fit these conditioned models with R-INLA. Then these models are combined using Bayesian model averaging to provide the final approximations to the posterior marginals of the model. Finally, we show some examples of the application of this technique in spatial statistics. It is worth noting that our approach can be extended to a number of other fields, and not only spatial statisticsnb_NO
dc.publisherAmerican Statistical Associationnb_NO
dc.rightsNavngivelse 3.0 Norge*
dc.subjectspatial statisticsnb_NO
dc.titleSpatial Data Analysis with R-INLA with Some Extensionsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.source.journalJournal of Statistical Softwarenb_NO

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Navngivelse  3.0 Norge
Except where otherwise noted, this item's license is described as Navngivelse 3.0 Norge