Bayesian Modeling of Air Pollution Extremes Using Nested Multivariate Max-Stable Processes
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Preprint Posting Date2018-03-18
Permanent link to this recordhttp://hdl.handle.net/10754/631987
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AbstractCapturing the potentially strong dependence among the peak concentrations of multiple air pollutants across a spatial region is crucial for assessing the related public health risks. In order to investigate the multivariate spatial dependence properties of air pollution extremes, we introduce a new class of multivariate max-stable processes. Our proposed model admits a hierarchical tree-based formulation, in which the data are conditionally independent given some latent nested positive stable random factors. The hierarchical structure facilitates Bayesian inference and offers a convenient and interpretable characterization. We fit this nested multivariate max-stable model to the maxima of air pollution concentrations and temperatures recorded at a number of sites in the Los Angeles area, showing that the proposed model succeeds in capturing their complex tail dependence structure.
CitationVettori S, Huser R, Genton MG (2019) Bayesian Modeling of Air Pollution Extremes Using Nested Multivariate Max-Stable Processes. Biometrics. Available: http://dx.doi.org/10.1111/biom.13051.
SponsorsThis research was supported by King Abdullah University of Science and Technology (KAUST), Saudi Arabia.