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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Martínez Beneito, Miguel Ángel
This document is a artículoDate2020
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.

    Martínez Beneito, Miguel Ángel 2021 Some links between conditional and coregionalized multivariate Gaussian Markov random fields Spatial Statistics 40 100383
https://doi.org/10.1016/j.spasta.2019.100383

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