# Fast Approximate Inference for Spatial Extreme Value Models

@inproceedings{Chen2021FastAI, title={Fast Approximate Inference for Spatial Extreme Value Models}, author={Mei-Ching Chen and Reza Ramezan and Martin Lysy}, year={2021} }

The generalized extreme value (GEV) distribution is a popular model for analyzing and forecasting extreme weather data. To increase prediction accuracy, spatial information is often pooled via a latent Gaussian process on the GEV parameters. Inference for such hierarchical GEV models is typically carried out using Markov chain Monte Carlo (MCMC) methods. However, MCMC can be prohibitively slow and computationally intensive when the number of latent variables is moderate to large. In this paper…

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SHOWING 1-10 OF 50 REFERENCES

Hierarchical modeling for extreme values observed over space and time

- MathematicsEnvironmental and Ecological Statistics
- 2007

We propose a hierarchical modeling approach for explaining a collection of spatially referenced time series of extreme values. We assume that the observations follow generalized extreme value (GEV)…

Latent Gaussian modeling and INLA: A review with focus on space-time applications

- Mathematics, Computer Science
- 2017

The principal theoretical concepts, model classes and inference tools within the INLA framework are reviewed, and a comprehensive simulation experiment is presented using simulated non Gaussian space-time count data with a first-order autoregressive dependence structure in time.

Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

- Mathematics
- 2009

Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive…

Bayesian Spatial Modeling of Extreme Precipitation Return Levels

- Mathematics
- 2007

Quantification of precipitation extremes is important for flood planning purposes, and a common measure of extreme events is the r-year return level. We present a method for producing maps of…

A hierarchical model for extreme wind speeds

- Computer Science
- 2006

A hierarchical model is developed for hourly gust maximum wind speed data, which attempts to identify site and seasonal effects for the marginal densities of hourly maxima, as well as for the serial dependence at each location.

A HIERARCHICAL MAX-STABLE SPATIAL MODEL FOR EXTREME PRECIPITATION.

- Mathematics, MedicineThe annals of applied statistics
- 2012

A new random effects model to account for spatial dependence is proposed and it is shown that the specification of the random effect distribution leads to a max-stable process that has the popular Gaussian extreme value process (GEVP) as a limiting case.

Bayesian hierarchical modeling of extreme hourly precipitation in Norway

- Environmental Science, Mathematics
- 2013

Spatial maps of extreme precipitation are a critical component of flood estimation in hydrological modeling, as well as in the planning and design of important infrastructure. This is particularly…

Spatial Regression Models for Extremes

- Mathematics
- 1999

Meteorological data are often recorded at a number of spatial locations. This gives rise to the possibility of pooling data through a spatial model to overcome some of the limitations imposed on an…

Continuous Spatial Process Models for Spatial Extreme Values

- Mathematics
- 2010

We propose a hierarchical modeling approach for explaining a collection of point-referenced extreme values. In particular, annual maxima over space and time are assumed to follow generalized extreme…

INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles

- Mathematics
- 2018

This work is motivated by the challenge organized for the 10th International Conference on Extreme-Value Analysis (EVA2017) to predict daily precipitation quantiles at the 99.8%$99.8\%$ level for…