Modelling spatially sampled proportion processes​​
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Modelling spatially sampled proportion processes​​

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Modelling spatially sampled proportion processes​​

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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.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.rights.uri info:eu-repo/semantics/openAccess
dc.source 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.subject Estadística bayesiana
dc.title Modelling spatially sampled proportion processes​​
dc.type info:eu-repo/semantics/article
dc.date.updated 2018-02-08T15:32:54Z
dc.identifier.idgrec 115731

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