Using non-homogeneous Poisson models with multiple change-points to estimate the number of ozone exceedances in Mexico City

作者：Achcar Jorge A; Rodrigues Eliane R^*; Tzintzun Guadalupe

来源：Environmetrics, 2011, 22(1): 1-12.

DOI：10.1002/env.1029

• Gibbs sampling
• Bayesian inference
• non-homogeneous Poisson model
• multiple change-points
• ozone air pollution
• Mexico city

In this paper, we consider some non-homogeneous Poisson models to estimate the probability that an air quality standard is exceeded a given number of times in a time interval of interest. We assume that the number of exceedances occurs according to a non-homogeneous Poisson process (NHPP). This Poisson process has rate function lambda(t), t >= 0, which depends on some parameters that must be estimated. We take into account two cases of rate functions: the Weibull and the Goel-Okumoto. We consider models with and without change-points. When the presence of change-points is assumed, we may have the presence of either one, two or three change-points, depending of the data set. The parameters of the rate functions are estimated using a Gibbs sampling algorithm. Results are applied to ozone data provided by the Mexico City monitoring network. In a first instance, we assume that there are no change-points present. Depending on the adjustment of the model, we assume the presence of either one, two or three change-points.

• 出版日期2011-2