High-dimensional modeling of spatial and spatio-temporal conditional extremes using INLA and Gaussian Markov random fields
Type
ArticleKAUST Grant Number
OSR-2017-CRG6-3434.02Date
2023-07-12Permanent link to this record
http://hdl.handle.net/10754/693015
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The conditional extremes framework allows for event-based stochastic modeling of dependent extremes, and has recently been extended to spatial and spatio-temporal settings. After standardizing the marginal distributions and applying an appropriate linear normalization, certain non-stationary Gaussian processes can be used as asymptotically-motivated models for the process conditioned on threshold exceedances at a fixed reference location and time. In this work, we adapt existing conditional extremes models to allow for the handling of large spatial datasets. This involves specifying the model for spatial observations at d locations in terms of a latent m≪d dimensional Gaussian model, whose structure is specified by a Gaussian Markov random field. We perform Bayesian inference for such models for datasets containing thousands of observation locations using the integrated nested Laplace approximation, or INLA. We explain how constraints on the spatial and spatio-temporal Gaussian processes, arising from the conditioning mechanism, can be implemented through the latent variable approach without losing the computationally convenient Markov property. We discuss tools for the comparison of models via their posterior distributions, and illustrate the flexibility of the approach with gridded Red Sea surface temperature data at over 6,000 observed locations. Posterior sampling is exploited to study the probability distribution of cluster functionals of spatial and spatio-temporal extreme episodes.Citation
Simpson, E. S., Opitz, T., & Wadsworth, J. L. (2023). High-dimensional modeling of spatial and spatio-temporal conditional extremes using INLA and Gaussian Markov random fields. Extremes. https://doi.org/10.1007/s10687-023-00468-8Sponsors
This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2017-CRG6-3434.02. The sea surface temperature data studied in this paper were provided by GHRSST Met Office and CMEMS.Publisher
Springer Science and Business Media LLCJournal
ExtremesAdditional Links
https://link.springer.com/10.1007/s10687-023-00468-8ae974a485f413a2113503eed53cd6c53
10.1007/s10687-023-00468-8