A Nonhomogeneous Poisson Process Geostatistical Model

dc.creatorCastro Morales
dc.creatorFidel Ernesto; Vicini
dc.creatorLorena; Hotta
dc.creatorLuiz K.; Achcar
dc.creatorJorge A.
dc.date2017
dc.datefev
dc.date2017-11-13T13:20:20Z
dc.date2017-11-13T13:20:20Z
dc.date.accessioned2018-03-29T05:54:02Z
dc.date.available2018-03-29T05:54:02Z
dc.identifierStochastic Environmental Research And Risk Assessment. Springer, v. 31, p. 493 - 507, 2017.
dc.identifier1436-3240
dc.identifier1436-3259
dc.identifierWOS:000395197800016
dc.identifier10.1007/s00477-016-1275-x
dc.identifierhttps://link.springer.com/article/10.1007/s00477-016-1275-x
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/327617
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1364642
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionThis 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.
dc.description31
dc.description2
dc.description493
dc.description507
dc.descriptionSao Paulo Research Foundation (FAPESP) [2009/15098-0]
dc.descriptionCNPq-Brazil
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.languageEnglish
dc.publisherSpringer
dc.publisherNew York
dc.relationStochastic Environmental Research and Risk Assessment
dc.rightsfechado
dc.sourceWOS
dc.subjectNonhomogeneous Poisson Processes
dc.subjectGeostatistical Data
dc.subjectCox Log-gaussian Process
dc.subjectBayesian Inference
dc.subjectMarkov Chain Monte Carlo
dc.subjectOzone Pollution
dc.titleA Nonhomogeneous Poisson Process Geostatistical Model
dc.typeArtículos de revistas


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