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non-homogeneous poisson processes Applied to Count Data:A Bayesian Approach Considering Different Prior Distributions

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Journal of Environmental Protection 2012年第10期3卷 1336-1345页

作者： Lorena Vicini Luiz K.Hotta Jorge A.Achcar Department of Statistics Federal University of Santa MariaSanta MariaBrazil Department of Statistics University of CampinasCampinasBrazil Department of Social Medicine Medical SchoolUniversity of Sao PauloRibeirao PretoBrazil

This article discusses the Bayesian approach for count data using non-homogeneous poisson processes, considering different prior distributions for the model parameters. A Bayesian approach using Markov Chain Monte Carlo (MCMC) simulation methods for this model was first introduced by [1], taking into account software reliability data and considering non-informative prior distributions for the parameters of the model. With the non-informative prior distributions presented by these authors, computational difficulties may occur when using MCMC methods. This article considers different prior distributions for the parameters of the proposed model, and studies the effect of such prior distributions on the convergence and accuracy of the results. In order to illustrate the proposed methodology, two examples are considered: the first one has simulated data, and the second has a set of data for pollution issues at a region in Mexico City.

关键词： non-homogeneous poisson processes Bayesian Analysis Markov Chain Monte Carlo Methods and Simulation Prior Distribution

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Modeling of Atmospheric Phenomena Using non-homogeneous poisson Process

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Journal of Environmental Science and Engineering(A) 2012年第8期1卷 1003-1006页

作者： Nahun Loya Hortensia Reyes Francisco Tajonar Francisco Ariza Department of Mathematics and Physies Autonomous University of Puebla Puebla 72570 Mexico Department of Mathematics Autonomous University of Guerrero Chilpancingo Guerrero 39070 Mexico

The PPNH （non-homogenous poisson processes） are frequently used as models for events that come about randomly in a given time period, for example, failure times, time of accidents occurrences, etc. In this work, PPNH is used to model monthly maximum observations of urban ozone corresponding to a period of five years from the meteorological stations of Merced, Pedregal and Plateros, located in the metropolitan area of Mexico City. The interest data are the times in which the observations surpassed the permissible level of ozone of 0.11 ppm, settled by the Mexican Official Norm （NOM-020-SSA 1-1993） to preserve public health.

关键词： Ozone non-homogeneous poisson processes environmental and chemical covariates.

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