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Modelling spatially sampled proportion processes

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Modelling spatially sampled proportion processes

dc.contributor.author	Paradinas Aranjuelo, Iosu
dc.contributor.author	Pennino, Maria Grazia
dc.contributor.author	López Quílez, Antonio
dc.contributor.author	Marín, Marcial
dc.contributor.author	Bellido Millán, José María
dc.contributor.author	Conesa Guillén, David
dc.date.accessioned	2018-02-08T15:32:54Z
dc.date.available	2018-02-08T15:32:54Z
dc.date.issued	2018
dc.identifier.citation	Paradinas Aranjuelo, Iosu Pennino, Maria Grazia López Quílez, Antonio Marín, Marcial Bellido Millán, José María Conesa Guillén, David 2018 Modelling spatially sampled proportion processes Revstat-Statistical Journal 16 1 71 86
dc.identifier.uri	http://hdl.handle.net/10550/64632
dc.description.abstract	Many ecological processes are measured as proportions and are spatially sampled. In all these cases the standard procedure has long been the transformation of proportional data with the arcsine square root or logit transformation, without considering the spatial correlation in any way. This paper presents a robust regression model to analyse this kind of data using a beta regression and including a spatially correlated term within the Bayesian framework. As a practical example, we apply the proposed approach to a spatio-temporally sampled fishery discard dataset.
dc.language.iso	eng
dc.relation.ispartof	Revstat-Statistical Journal, 2018, vol. 16, num. 1, p. 71-86
dc.subject	Estadística bayesiana
dc.title	Modelling spatially sampled proportion processes
dc.type	journal article	es_ES
dc.date.updated	2018-02-08T15:32:54Z
dc.identifier.idgrec	115731
dc.rights.accessRights	open access	es_ES