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

✍ Scribed by Jorge A. Achcar; Eliane R. Rodrigues; Guadalupe Tzintzun

• Publisher: John Wiley and Sons
• Year: 2011
• Tongue: English
• Weight: 320 KB
• Volume: 22
• Category: Article
• ISSN: 1180-4009
• DOI: 10.1002/env.1029

✦ Synopsis

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 λ(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.