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Vol.4 , No. 6, Publication Date: Dec. 5, 2017, Page: 60-70
. The proposed approach uses the Smith and Schlather dependence models which are fitted in each year and, based on the Takeuchi information criterion, the Schlather model was chosen as the best fitted for these data. Subsequently, we propose a trend surfaces parameter nested into the parameters of the extreme values distribution, that allows to obtain predictive maps for this pollutant. The results of the studied case indicate that Schlather dependence models shows the best fit, and predictive maps shows an increase in the levels of this pollutant in the south region of the urban studied area.&picture=http://www.aascit.org/aascit/decorators/img/journal/924.jpg)
| [1] | José del Carmen Jiménez Hernández, Instituto de Física y Matemáticas, Universidad Tecnológica de la Mixteca, Huajuapan de León, Oaxaca, México. |
| [2] | Humberto Vaquera Huerta, Departamento de Estadística, Colegio de Postgraduados, Texcoco, Edo. De, México. |
| [3] | Paulino Pérez Rodríguez, Departamento de Estadística, Colegio de Postgraduados, Texcoco, Edo. De, México. |
Extreme value models has been applied in environmental studies for modeling air pollution data. Recently, the max-stable processes have become a useful tool for statistical modeling of spatial behavior of extremes. In this article, the max-stable processes models are applied to data of extreme concentration levels of carbon monoxide in order to investigate spatial trends of this air pollutant in one of the largest urban zone in the world (Mexico City). The proposed approach uses the Smith and Schlather dependence models which are fitted in each year and, based on the Takeuchi information criterion, the Schlather model was chosen as the best fitted for these data. Subsequently, we propose a trend surfaces parameter nested into the parameters of the extreme values distribution, that allows to obtain predictive maps for this pollutant. The results of the studied case indicate that Schlather dependence models shows the best fit, and predictive maps shows an increase in the levels of this pollutant in the south region of the urban studied area.
Keywords
Extreme Value Distribution, Stochastic Process, Likelihood Inference, Spatial Dependence
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