Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/11387
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Campo DC | Valor | Idioma |
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dc.contributor.author | Diggle, Peter | - |
dc.contributor.author | Menezes, Raquel | - |
dc.contributor.author | Su Ting-li | - |
dc.date.accessioned | 2010-12-22T15:02:05Z | - |
dc.date.available | 2010-12-22T15:02:05Z | - |
dc.date.issued | 2010 | - |
dc.date.submitted | 2009 | - |
dc.identifier.citation | "Journal of Royal Statistics Society. Series C". ISSN 1467-9876. 59:2 (2010) 191-232. | por |
dc.identifier.issn | 1467-9876 | por |
dc.identifier.uri | https://hdl.handle.net/1822/11387 | - |
dc.description.abstract | Geostatistics involves the fitting of spatially continuous models to spatially discrete data (Chil`es and Delfiner, 1999). Preferential sampling arises when the process that determines the data-locations and the process being modelled are stochastically dependent. Conventional geostatistical methods assume, if only implicitly, that sampling is non-preferential. However, these methods are often used in situations where sampling is likely to be preferential. For example, in mineral exploration samples may be concentrated in areas thought likely to yield high-grade ore. We give a general expression for the likelihood function of preferentially sampled geostatistical data, and describe how this can be evaluated approximately using Monte Carlo methods. We present a model for preferential sampling, and demonstrate through simulated examples that ignoring preferential sampling can lead to misleading inferences. We describe an application of the model to a set of bio-monitoring data from Galicia, northern Spain, in which making allowance for preferential sampling materially changes the results of the analysis. | por |
dc.description.sponsorship | This work was supported by the UK Engineering and Physical Sciences Research Council through the award of a Senior Fellowship to Peter Diggle.We thank the Ecotoxicology Group, University of Santiago de Compostela, for permission to use the Galicia data and, in particular, Jose Angel Fernandez, for helpful discussions concerning the data.We also thank Havard Rue for advice on efficient conditional simulation of spatially continuous Gaussian processes. | por |
dc.language.iso | eng | por |
dc.publisher | Wiley | por |
dc.rights | openAccess | por |
dc.subject | Environmental monitoring | por |
dc.subject | Geostatistics | por |
dc.subject | Log-Gaussian Cox process | por |
dc.subject | Preferential sampling | por |
dc.subject | Marked point process | por |
dc.subject | Monte Carlo inference | por |
dc.title | Geostatistical inference under preferential sampling | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | http://www3.interscience.wiley.com/journal/117997424/home | por |
sdum.number | 2 | por |
sdum.pagination | 191-232 | por |
sdum.publicationstatus | published | por |
sdum.volume | 59 | por |
oaire.citationStartPage | 191 | por |
oaire.citationEndPage | 232 | por |
oaire.citationIssue | 2 | por |
oaire.citationVolume | 59 | por |
dc.identifier.doi | 10.1111/j.1467-9876.2009.00701.x | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | Journal of Royal Statistics Society, Series C | por |
Aparece nas coleções: | CMAT - Artigos em revistas com arbitragem / Papers in peer review journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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geopref_revised_final.pdf | Documento principal | 1,01 MB | Adobe PDF | Ver/Abrir |