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dc.contributor.authorGómez-Rubio, Virgilio
dc.contributor.authorBivand, Roger S.
dc.contributor.authorRue, Haavard
dc.date.accessioned2020-05-21T12:08:16Z
dc.date.available2019-12-19T13:04:53Z
dc.date.available2020-05-21T12:08:16Z
dc.date.issued2020-06-01
dc.date.submitted2019-10-25
dc.identifier.citationGómez-Rubio, V., Bivand, R. S., & Rue, H. (2020). Bayesian Model Averaging with the Integrated Nested Laplace Approximation. Econometrics, 8(2), 23. doi:10.3390/econometrics8020023
dc.identifier.issn2225-1146
dc.identifier.doi10.3390/econometrics8020023
dc.identifier.urihttp://hdl.handle.net/10754/660707
dc.description.abstractThe integrated nested Laplace approximation (INLA) for Bayesian inference is an efficient approach to estimate the posterior marginal distributions of the parameters and latent effects of Bayesian hierarchical models that can be expressed as latent Gaussian Markov random fields (GMRF). The representation as a GMRF allows the associated software R-INLA to estimate the posterior marginals in a fraction of the time as typical Markov chain Monte Carlo algorithms. INLA can be extended by means of Bayesian model averaging (BMA) to increase the number of models that it can fit to conditional latent GMRF. In this paper, we review the use of BMA with INLA and propose a new example on spatial econometrics models.
dc.description.sponsorshipVirgilio Gómez-Rubio was funded by Consejería de Educación, Cultura y Deportes (JCCM, Spain) and FEDER, Grant Number SBPLY/17/180501/000491, as well as by Ministerio de Economía y Competitividad (Spain), Grant Number MTM2016-77501-P.
dc.publisherMDPI AG
dc.relation.urlhttps://www.mdpi.com/2225-1146/8/2/23
dc.relation.urlhttps://www.mdpi.com/2225-1146/8/2/23/pdf
dc.rightsThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleBayesian model averaging with the integrated nested laplace approximation
dc.typeArticle
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalEconometrics
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Mathematics, School of Industrial Engineering, Universidad de Castilla-La Mancha, E-02071, Albacete, Spain
dc.contributor.institutionDepartment of Economics, Norwegian School of Economics, 5045, Bergen, Norway
dc.identifier.volume8
dc.identifier.issue2
dc.identifier.pages1-15
dc.identifier.arxivid1911.00797
kaust.personRue, Haavard
dc.date.accepted2020-05-20
dc.identifier.eid2-s2.0-85086019075
refterms.dateFOA2019-12-19T13:05:19Z
dc.date.posted2019-11-02


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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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