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Bayesian inference in multivariate spatio-temporal areal models using INLA: analysis of gender-based violence in small areas
dc.creator | Vicente Fuenzalida, Gonzalo | es_ES |
dc.creator | Goicoa Mangado, Tomás | es_ES |
dc.creator | Ugarte Martínez, María Dolores | es_ES |
dc.date.accessioned | 2020-07-02T06:50:31Z | |
dc.date.available | 2020-07-02T06:50:31Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 1436-3240 | |
dc.identifier.uri | https://hdl.handle.net/2454/37270 | |
dc.description.abstract | Multivariate models for spatial count data are currently receiving attention in disease mapping to model two or more diseases jointly. They have been thoroughly studied from a theoretical point of view, but their use in practice is still limited because they are computationally expensive and, in general, they are not implemented in standard software to be used routinely. Here, a new multivariate proposal, based on the recently derived M models for spatial data, is developed for spatio-temporal areal data. The model takes account of the correlation between the spatial and temporal patterns of the phenomena being studied, and it also includes spatio-temporal interactions. Though multivariate models have been traditionally fitted using Markov chain Monte Carlo techniques, here we propose to adopt integrated nested Laplace approximations to speed up computations as results obtained using both fitting techniques were nearly identical. The techniques are used to analyse two forms of crimes against women in India. In particular, we focus on the joint analysis of rapes and dowry deaths in Uttar Pradesh, the most populated Indian state, during the years 2001-2014. | en |
dc.description.sponsorship | This work has been supported by Project MTM2017-82553-R (AEI/ FEDER, UE). It has also been partially funded by la Caixa Foundation (ID 1000010434), Caja Navarra Foundation, and UNED Pamplona, under agreement LCF/PR/PR15/51100007. | en |
dc.format.extent | 20 p. | |
dc.format.mimetype | application/pdf | en |
dc.language.iso | eng | en |
dc.publisher | Springer | en |
dc.relation.ispartof | Stochastic Environmental Research and Risk Assessment, 2020 | en |
dc.rights | © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | en |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Crimes against women | en |
dc.subject | Dowry deaths | en |
dc.subject | Rapes | en |
dc.subject | Gibbs sampling | en |
dc.subject | Hierarchical Bayesian models | en |
dc.subject | INLA | en |
dc.subject | M-models | en |
dc.subject | WinBUGS | en |
dc.title | Bayesian inference in multivariate spatio-temporal areal models using INLA: analysis of gender-based violence in small areas | en |
dc.type | info:eu-repo/semantics/article | en |
dc.type | Artículo / Artikulua | es |
dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
dc.contributor.department | Institute for Advanced Materials and Mathematics - INAMAT2 | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | en |
dc.rights.accessRights | Acceso abierto / Sarbide irekia | es |
dc.identifier.doi | 10.1007/s00477-020-01808-x | |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2017-82553-R/ES/ | en |
dc.relation.publisherversion | https://doi.org/10.1007/s00477-020-01808-x | |
dc.type.version | info:eu-repo/semantics/publishedVersion | en |
dc.type.version | Versión publicada / Argitaratu den bertsioa | es |
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La licencia del ítem se describe como © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.