Improved return level estimation via a weighted likelihood, latent spatial extremes model

10/16/2018
by   Joshua Hewitt, et al.
0

Uncertainty in return level estimates for rare events, like the intensity of large rainfall events, makes it difficult to develop strategies to mitigate related hazards, like flooding. Latent spatial extremes models reduce uncertainty by exploiting spatial dependence in statistical characteristics of extreme events to borrow strength across locations. However, these estimates can have poor properties due to model misspecification: many latent spatial extremes models do not account for extremal dependence, which is spatial dependence in the extreme events themselves. We improve estimates from latent spatial extremes models that make conditional independence assumptions by proposing a weighted likelihood that uses the extremal coefficient to incorporate information about extremal dependence during estimation. This approach differs from, and is more simple than, directly modeling the spatial extremal dependence; for example, by fitting a max-stable process, which are challenging to fit to real, large datasets. We adopt a hierarchical Bayesian framework for inference, use simulation to show the weighted model provides improved estimates of high quantiles, and apply our model to improve return level estimates for Colorado rainfall events with 1 probability.

READ FULL TEXT
10/05/2017

A Bayesian spatial hierarchical model for extreme precipitation in Great Britain

Intense precipitation events are commonly known to be associated with an...
06/07/2019

Modelling the spatial extent and severity of extreme European windstorms

Windstorms are a primary natural hazard affecting Europe that are common...
07/19/2020

Integration of max-stable processes and Bayesian model averaging to predict extreme climatic events in multi-model ensembles

Projections of changes in extreme climate are sometimes predicted by usi...
07/05/2021

On the Estimation of Bivariate Return Curves for Extreme Values

In the multivariate setting, defining extremal risk measures is importan...
06/12/2019

A Bayesian Hierarchical Model for Evaluating Forensic Footwear Evidence

When a latent shoeprint is discovered at a crime scene, forensic analyst...
01/26/2020

Inference on extremal dependence in a latent Markov tree model attracted to a Hüsler-Reiss distribution

A Markov tree is a probabilistic graphical model for a random vector by ...