The conditional spatial extremes model of Wadsworth and Tawn (2022), which focuses on extreme events given threshold exceedance at a site, has garnered a lot of attention as a flexible way to model large-scale spatio-temporal events.
We consider extensions that combine Gaussian Markov random field residual processes along with data augmentation schemes for dealing with left-censored realizations, exploiting the sparsity of the precision matrix obtained through the basis function approximation of the Gaussian process. Models are fitted using Markov chain Monte Carlo methods and we showcase the scalability of the approach using precipitation data from British Columbia. This is joint work with Rishikesh Yadav and Nicholas Beck.
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Secretariat Econometrics: eb-secr@ese.eur.nl