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On the convenience of heteroscedasticity in highly multivariate disease mapping

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On the convenience of heteroscedasticity in highly multivariate disease mapping

dc.contributor.author	Corpas Burgos, Francisca
dc.contributor.author	Botella-Rocamora, Paloma
dc.contributor.author	Martínez Beneito, Miguel Ángel
dc.date.accessioned	2021-02-08T15:10:11Z
dc.date.available	2021-02-08T15:10:11Z
dc.date.issued	2019
dc.identifier.uri	https://hdl.handle.net/10550/77712
dc.description.abstract	Highly multivariate disease mapping has recently been proposed as an enhancement of traditional multivariate studies, making it possible to perform the joint analysis of a large number of diseases. This line of research has an important potential since it integrates the information of many diseases into a single model yielding richer and more accurate risk maps. In this paper we show how some of the proposals already put forward in this area display some particular problems when applied to small regions of study. Specifically, the homoscedasticity of these proposals may produce evident misfits and distorted risk maps. In this paper we propose two new models to deal with the variance-adaptivity problem in multivariate disease mapping studies and give some theoretical insights on their interpretation.
dc.language.iso	eng
dc.relation.ispartof	Test, 2019, vol. 28, p. 1229-1250
dc.source	Corpas Burgos, Francisca Botella-Rocamora, Paloma Martínez Beneito, Miguel Ángel 2019 On the convenience of heteroscedasticity in highly multivariate disease mapping Test 28 1229 1250
dc.subject	Estadística bayesiana
dc.subject	Malalties
dc.title	On the convenience of heteroscedasticity in highly multivariate disease mapping
dc.type	journal article	es_ES
dc.date.updated	2021-02-08T15:10:11Z
dc.identifier.doi	https://doi.org/10.1007/s11749-019-00628-8
dc.identifier.idgrec	137543
dc.rights.accessRights	open access	es_ES