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arXiv · 1804.04588

Bayesian Modeling of Air Pollution Extremes Using Nested Multivariate Max-Stable Processes

Sabrina Vettori·Raphaël Huser·Marc G. Genton
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Abstract

Capturing 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 $\alpha$-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.

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Sabrina Vettori, Raphaël Huser, Marc G. Genton. 2018-03-18. Bayesian Modeling of Air Pollution Extremes Using Nested Multivariate Max-Stable Processes. https://arxiv.org/abs/1804.04588

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