Modelling spatial patterns of distribution and abundance of mussel seed using structured additive regression models
As mussel farming depends on sources of natural mussel seed, knowledge of factors is required to regulate both the spatial distribution and abundance of this resource. These spatial patterns were modelled using Bayesian STructured Additive Regression (STAR) models for categorical data, based on a mi...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2010 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/11035 |
| Acceso en línea: | https://hdl.handle.net/2099/11035 |
| Access Level: | acceso abierto |
| Palabra clave: | Mathematical statistics Mussel seed Bayesian structured additive regression (STAR) models Spatial effects Bayesian P-splines Estadística matemàtica 62 Statistics::62F Parametric inference Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
| Sumario: | As mussel farming depends on sources of natural mussel seed, knowledge of factors is required to regulate both the spatial distribution and abundance of this resource. These spatial patterns were modelled using Bayesian STructured Additive Regression (STAR) models for categorical data, based on a mixed-model representation. We used Bayesian penalized splines for modelling the continuous covariate effects and a Markov random field prior for estimating the spatial effects. |
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