Geostatistical inference under preferential sampling
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/11387 |
Resumo: | 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. |
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Geostatistical inference under preferential samplingEnvironmental monitoringGeostatisticsLog-Gaussian Cox processPreferential samplingMarked point processMonte Carlo inferenceScience & TechnologyGeostatistics 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.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.WileyUniversidade do MinhoDiggle, PeterMenezes, RaquelSu Ting-li20102010-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/11387eng"Journal of Royal Statistics Society. Series C". ISSN 1467-9876. 59:2 (2010) 191-232.1467-987610.1111/j.1467-9876.2009.00701.xhttp://www3.interscience.wiley.com/journal/117997424/homeinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:32:22Zoai:repositorium.sdum.uminho.pt:1822/11387Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:27:42.747152Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Geostatistical inference under preferential sampling |
title |
Geostatistical inference under preferential sampling |
spellingShingle |
Geostatistical inference under preferential sampling Diggle, Peter Environmental monitoring Geostatistics Log-Gaussian Cox process Preferential sampling Marked point process Monte Carlo inference Science & Technology |
title_short |
Geostatistical inference under preferential sampling |
title_full |
Geostatistical inference under preferential sampling |
title_fullStr |
Geostatistical inference under preferential sampling |
title_full_unstemmed |
Geostatistical inference under preferential sampling |
title_sort |
Geostatistical inference under preferential sampling |
author |
Diggle, Peter |
author_facet |
Diggle, Peter Menezes, Raquel Su Ting-li |
author_role |
author |
author2 |
Menezes, Raquel Su Ting-li |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Diggle, Peter Menezes, Raquel Su Ting-li |
dc.subject.por.fl_str_mv |
Environmental monitoring Geostatistics Log-Gaussian Cox process Preferential sampling Marked point process Monte Carlo inference Science & Technology |
topic |
Environmental monitoring Geostatistics Log-Gaussian Cox process Preferential sampling Marked point process Monte Carlo inference Science & Technology |
description |
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. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010 2010-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/11387 |
url |
http://hdl.handle.net/1822/11387 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
"Journal of Royal Statistics Society. Series C". ISSN 1467-9876. 59:2 (2010) 191-232. 1467-9876 10.1111/j.1467-9876.2009.00701.x http://www3.interscience.wiley.com/journal/117997424/home |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Wiley |
publisher.none.fl_str_mv |
Wiley |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799132769486372864 |