Some links between conditional and coregionalized multivariate Gaussian Markov random fields
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Some links between conditional and coregionalized multivariate Gaussian Markov random fields

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Some links between conditional and coregionalized multivariate Gaussian Markov random fields

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dc.contributor.author Martínez Beneito, Miguel Ángel
dc.date.accessioned 2021-02-08T17:16:18Z
dc.date.available 2021-02-08T17:16:18Z
dc.date.issued 2020
dc.identifier.uri https://hdl.handle.net/10550/77715
dc.description.abstract Multivariate disease mapping models are attracting considerable attention. Many modeling proposals have been made in this area, which could be grouped into three large sets: coregionalization, multivariate conditional and univariate conditional models. In this work we establish some links between these three groups of proposals. Specifically, we explore the equivalence between the two conditional approaches and show that an important class of coregionalization models can be seen as a large subclass of the conditional approaches. Additionally, we propose an extension to the current set of coregionalization models with some new unexplored proposals. This extension is able to reproduce asymmetric cross-spatial covariances for different diseases. This shows that the previously accepted belief that coregionalization was not able to reproduce models with asymmetric cross-covariances was wrong.
dc.language.iso eng
dc.relation.ispartof Spatial Statistics, 2021, vol. 40, num. 100383
dc.rights.uri info:eu-repo/semantics/openAccess
dc.source Martínez Beneito, Miguel Ángel 2021 Some links between conditional and coregionalized multivariate Gaussian Markov random fields Spatial Statistics 40 100383
dc.subject Estadística bayesiana
dc.subject Malalties
dc.title Some links between conditional and coregionalized multivariate Gaussian Markov random fields
dc.type info:eu-repo/semantics/article
dc.date.updated 2021-02-08T17:16:18Z
dc.identifier.doi https://doi.org/10.1016/j.spasta.2019.100383
dc.identifier.idgrec 143217

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