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Bayesian modeling of air pollution extremes using nested multivariate max‐stable processes

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  • Sabrina Vettori
  • Raphaël Huser
  • Marc G. Genton

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.

Suggested Citation

  • Sabrina Vettori & Raphaël Huser & Marc G. Genton, 2019. "Bayesian modeling of air pollution extremes using nested multivariate max‐stable processes," Biometrics, The International Biometric Society, vol. 75(3), pages 831-841, September.
    • Handle: RePEc:bla:biomet:v:75:y:2019:i:3:p:831-841
    DOI: 10.1111/biom.13051
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    Cited by:

    1. Daniela Castro‐Camilo & Raphaël Huser & Håvard Rue, 2022. "Practical strategies for generalized extreme value‐based regression models for extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 33(6), September.
    2. Nurulkamal Masseran, 2022. "Multifractal Characteristics on Temporal Maximum of Air Pollution Series," Mathematics, MDPI, vol. 10(20), pages 1-15, October.
    3. Rishikesh Yadav & Raphaël Huser & Thomas Opitz, 2021. "Spatial hierarchical modeling of threshold exceedances using rate mixtures," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.

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