Details

Autor(en) / Beteiligte

• Vettori, Sabrina
• Huser, Raphaël
• Genton, Marc G.

Titel

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

Ist Teil von

• Biometrics, 2019-09, Vol.75 (3), p.831-841

Ort / Verlag

United States: Blackwell Publishing Ltd

Erscheinungsjahr

2019

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.