High-dimensional modeling of spatial and spatio-temporal conditional extremes using INLA and the SPDE approach

11/09/2020
by   Emma S. Simpson, et al.
0

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 adopt a Bayesian perspective by implementing estimation through the integrated nested Laplace approximation (INLA), allowing for novel and flexible semi-parametric specifications of the Gaussian mean function. By using Gauss-Markov approximations of the Matérn covariance function (known as the Stochastic Partial Differential Equation approach) at a latent stage of the model, likelihood-based inference becomes feasible even with several thousands of observed locations. 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 posterior models, 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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

10/17/2016

Spatio-temporal Gaussian processes modeling of dynamical systems in systems biology

Quantitative modeling of post-transcriptional regulation process is a ch...
10/06/2021

Latent Gaussian Models for High-Dimensional Spatial Extremes

In this chapter, we show how to efficiently model high-dimensional extre...
09/22/2020

Spatio-temporal modelling of PM_10 daily concentrations in Italy using the SPDE approach

This paper illustrates the main results of a spatio-temporal interpolati...
03/26/2020

Gaussian-Dirichlet Random Fields for Inference over High Dimensional Categorical Observations

We propose a generative model for the spatio-temporal distribution of hi...
03/18/2020

Modeling of Multisite Precipitation Occurrences Using Latent Gaussian-based Multivariate Binary Response Time Series

A new stochastic model for daily precipitation occurrence processes obse...
02/19/2020

Point-process based Bayesian modeling of space-time structures of forest fire occurrences in Mediterranean France

Due to climate change and human activity, wildfires are expected to beco...
02/14/2022

Joint Modeling and Prediction of Massive Spatio-Temporal Wildfire Count and Burnt Area Data with the INLA-SPDE Approach

This paper describes the methodology used by the team RedSea in the data...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.