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

Sabrina Vettori, Raphaël Huser, Marc G. Genton

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

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 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.
Original languageEnglish (US)
Pages (from-to)831-841
Number of pages11
JournalBiometrics
Volume75
Issue number3
DOIs
StatePublished - Apr 22 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-04-23
Acknowledgements: This research was supported by King Abdullah University of Science and Technology (KAUST), Saudi Arabia.

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