A flexible Bayesian hierarchical modeling framework for spatially dependent peaks-over-threshold data

Rishikesh Yadav, Raphaël Huser, Thomas Opitz

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


In this work, we develop a constructive modeling framework for extreme threshold exceedances in repeated observations of spatial fields, based on general product mixtures of random fields possessing light or heavy-tailed margins and various spatial dependence characteristics, which are suitably designed to provide high flexibility in the tail and at sub-asymptotic levels. Our proposed model is akin to a recently proposed Gamma–Gamma model using a ratio of processes with Gamma marginal distributions, but it possesses a higher degree of flexibility in its joint tail structure, capturing strong dependence more easily. We focus on constructions with the following three product factors, whose different roles ensure their statistical identifiability: a heavy-tailed spatially-dependent field, a lighter-tailed spatially-constant field, and another lighter-tailed spatially-independent field. Thanks to the model's hierarchical formulation, inference may be conveniently performed based on Markov chain Monte Carlo methods. We leverage the Metropolis adjusted Langevin algorithm (MALA) with random block proposals for latent variables, as well as the stochastic gradient Langevin dynamics (SGLD) algorithm for hyperparameters, in order to fit our proposed model very efficiently in relatively high spatio-temporal dimensions, while simultaneously censoring non-exceedances of the threshold and performing spatial prediction at multiple sites. The censoring mechanism is applied to the spatially independent component, such that only univariate cumulative distribution functions have to be evaluated. We explore the theoretical properties of our model, and illustrate the proposed methodology by simulation and application to daily precipitation data from North–Eastern Spain measured at nearly 100 stations over the period 2011–2020.
Original languageEnglish (US)
Pages (from-to)100672
JournalSpatial Statistics
StatePublished - Jun 10 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-06-20
Acknowledged KAUST grant number(s): OSR-CRG2017-3434, OSR-CRG2020-4394
Acknowledgements: This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Awards No. OSR-CRG2017-3434 and No. OSR-CRG2020-4394, Saudi Arabia.

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Statistics and Probability
  • Management, Monitoring, Policy and Law


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