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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampHotta, Luiz Koodipt_BR
dc.typeArtigopt_BR
dc.titleA nonhomogeneous poisson process geostatistical modept_BR
dc.contributor.authorCastro Morales, Fidel Ernestopt_BR
dc.contributor.authorVicini, Lorenapt_BR
dc.contributor.authorHotta, Luiz K.pt_BR
dc.contributor.authorAchcar, Jorge A.pt_BR
dc.subjectMétodos MCMC (Estatística)pt_BR
dc.subjectDistribuição de Poissonpt_BR
dc.subjectProcessos de Markovpt_BR
dc.subjectInferência bayesianapt_BR
dc.subject.otherlanguageMCMC methods (Statistics)pt_BR
dc.subject.otherlanguagePoisson distributionpt_BR
dc.subject.otherlanguageMarkov processespt_BR
dc.subject.otherlanguageBayesian inferencept_BR
dc.description.abstractThis paper introduces a new geostatistical model for counting data under a space-time approach using nonhomogeneous Poisson processes, where the random intensity process has an additive formulation with two components: a Gaussian spatial component and a component accounting for the temporal effect. Inferences of interest for the proposed model are obtained under the Bayesian paradigm. To illustrate the usefulness of the proposed model, we first develop a simulation study to test the efficacy of the Markov Chain Monte Carlo (MCMC) method to generate samples for the joint posterior distribution of the model's parameters. This study shows that the convergence of the MCMC algorithm used to simulate samples for the joint posterior distribution of interest is easily obtained for different scenarios. As a second illustration, the proposed model is applied to a real data set related to ozone air pollution collected in 22 monitoring stations in Mexico City in the 2010 year. The proposed geostatistical model has good performance in the data analysis, in terms of fit to the data and in the identification of the regions with the highest pollution levels, that is, the southwest, the central and the northwest regions of Mexico City.pt_BR
dc.relation.ispartofStochastic environmental research and risk assessmentpt_BR
dc.publisher.cityHeidelbergpt_BR
dc.publisher.countryAlemanhapt_BR
dc.publisherSpringerpt_BR
dc.date.issued2017pt_BR
dc.date.monthofcirculationfeb.pt_BR
dc.identifier.citationStochastic Environmental Research And Risk Assessment. Springer, v. 31, p. 493 - 507, 2017.pt_BR
dc.language.isoengpt_BR
dc.description.volume31pt_BR
dc.description.issuenumber2pt_BR
dc.description.firstpage493pt_BR
dc.description.lastpage507pt_BR
dc.rightsfechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn1436-3240pt_BR
dc.identifier.eissn1436-3259pt_BR
dc.identifier.doi10.1007/s00477-016-1275-xpt_BR
dc.identifier.urlhttps://link.springer.com/article/10.1007/s00477-016-1275-xpt_BR
dc.description.sponsorshipFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPpt_BR
dc.description.sponsorship1FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOpt_BR
dc.description.sponsordocumentnumber2009/15098-0pt_BR
dc.date.available2017-11-13T13:20:20Z-
dc.date.accessioned2017-11-13T13:20:20Z-
dc.description.provenanceMade available in DSpace on 2017-11-13T13:20:20Z (GMT). No. of bitstreams: 1 000395197800016.pdf: 1903551 bytes, checksum: 448147306bc0aafc58d6a179437704a5 (MD5) Previous issue date: 2017 Bitstreams deleted on 2020-07-15T20:00:23Z: 000395197800016.pdf,. Added 1 bitstream(s) on 2020-07-15T20:21:37Z : No. of bitstreams: 1 000395197800016.pdf: 1956781 bytes, checksum: 588fc3c12c9045038263945d5b31366a (MD5)en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/327617-
dc.contributor.departmentDepartamento de Estatísticapt_BR
dc.contributor.unidadeInstituto de Matemática, Estatística e Computação Científicapt_BR
dc.subject.keywordNonhomogeneous poisson processespt_BR
dc.subject.keywordGeostatistical datapt_BR
dc.subject.keywordCox log-gaussian processpt_BR
dc.subject.keywordOzone pollutionpt_BR
dc.subject.keywordMarkov chain monte carlopt_BR
dc.subject.keywordBayesian inferencept_BR
dc.identifier.source000395197800016pt_BR
dc.creator.orcid0000-0002-1005-602Xpt_BR
dc.type.formArtigopt_BR
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