Multivariate Bayesian spatio-temporal P-spline models to analyze crimes against women

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dc.contributor.authorVicente Fuenzalida, Gonzalo
dc.contributor.authorGoicoa Mangado, Tomás
dc.contributor.authorUgarte Martínez, María Dolores
dc.contributor.departmentEstatistika, Informatika eta Matematikaeu
dc.contributor.departmentInstitute for Advanced Materials and Mathematics - INAMAT2en
dc.contributor.departmentEstadística, Informática y Matemáticases_ES
dc.date.accessioned2022-08-04T07:58:08Z
dc.date.available2022-08-04T07:58:08Z
dc.date.issued2021
dc.date.updated2022-08-04T07:23:40Z
dc.description.abstractUnivariate spatio-temporal models for areal count data have received great attention in recent years for estimating risks. However, models for studying multivariate responses are less commonly used mainly due to the computational burden. In this article, multivariate spatio-temporal P-spline models are proposed to study different forms of violence against women. Modeling distinct crimes jointly improves the precision of estimates over univariate models and allows to compute correlations among them. The correlation between the spatial and the temporal patterns may suggest connections among the different crimes that will certainly benefit a thorough comprehension of this problem that affects millions of women around the world. The models are fitted using integrated nested Laplace approximations and are used to analyze four distinct crimes against women at district level in the Indian state of Maharashtra during the period 2001-2013.en
dc.description.sponsorshipProject MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.130 39/501100011033.en
dc.format.mimetypeapplication/pdfen
dc.identifier.citationVicente, G.; Goicoa, T.; Ugarte, M. D.. (2021). Multivariate Bayesian spatio-temporal P-spline models to analyze crimes against women. Biostatistics .en
dc.identifier.doi10.1093/biostatistics/kxab042
dc.identifier.issn1465-4644
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/43670
dc.language.isoengen
dc.publisherOxford University Pressen
dc.relation.ispartofBiostatistics, 2021en
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2017-82553-R/ES/
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113125RB-I00/ES/
dc.relation.publisherversionhttps://doi.org/10.1093/biostatistics/kxab042
dc.rights© The Author 2021. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licenseen
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBayesian inferenceen
dc.subjectGender-based violenceen
dc.subjectINLAen
dc.subjectSmoothingen
dc.subjectSpatio-temporal patternsen
dc.titleMultivariate Bayesian spatio-temporal P-spline models to analyze crimes against womenen
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication
relation.isAuthorOfPublication77ba75f3-a30d-4f01-8cc5-9de6d3a10d8d
relation.isAuthorOfPublicatione87ff19e-9d36-4286-989b-cafd391dff9d
relation.isAuthorOfPublication.latestForDiscovery77ba75f3-a30d-4f01-8cc5-9de6d3a10d8d

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