Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/327617
Type: Artigo
Title: A Nonhomogeneous Poisson Process Geostatistical Model
Author: Castro Morales
Fidel Ernesto; Vicini
Lorena; Hotta
Luiz K.; Achcar
Jorge A.
Abstract: This 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.
Subject: Nonhomogeneous Poisson Processes
Geostatistical Data
Cox Log-gaussian Process
Bayesian Inference
Markov Chain Monte Carlo
Ozone Pollution
Editor: Springer
New York
Citation: Stochastic Environmental Research And Risk Assessment. Springer, v. 31, p. 493 - 507, 2017.
Rights: fechado
Identifier DOI: 10.1007/s00477-016-1275-x
Address: https://link.springer.com/article/10.1007/s00477-016-1275-x
Date Issue: 2017
Appears in Collections:Unicamp - Artigos e Outros Documentos

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