Modelling extremes of spatial aggregates of precipitation using conditional methods
From MaRDI portal
Publication:2080785
DOI10.1214/22-AOAS1609zbMath1498.62291arXiv2102.10906OpenAlexW3133324820MaRDI QIDQ2080785
Jordan Richards, Jonathan A. Tawn, Simon J. Brown
Publication date: 10 October 2022
Published in: The Annals of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2102.10906
Inference from spatial processes (62M30) Applications of statistics to environmental and related topics (62P12) Extreme value theory; extremal stochastic processes (60G70) Statistics of extreme values; tail inference (62G32)
Related Items
A marginal modelling approach for predicting wildfire extremes across the contiguous United States, On the tail behaviour of aggregated random variables
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A hierarchical max-stable spatial model for extreme precipitation
- Some models for rainfall based on stochastic point processes
- Non-stationary dependence structures for spatial extremes
- Spatial modeling of extreme snow depth
- Exceedance probability of the integral of a stochastic process
- Basin-wide spatial conditional extremes for severe ocean storms
- On the tail behaviour of aggregated random variables
- Spatial hierarchical modeling of precipitation extremes from a regional climate model
- On spatial extremes: with application to a rainfall problem
- Ordered Multivariate Extremes
- The Stationary Bootstrap
- High-dimensional peaks-over-threshold inference
- A Conditional Approach for Multivariate Extreme Values (with Discussion)
- Local Likelihood Estimation of Complex Tail Dependence Structures, Applied to U.S. Precipitation Extremes
- Generalized Additive Models for Exceedances of High Thresholds With an Application to Return Level Estimation for U.S. Wind Gusts
- Likelihood-Based Inference for Max-Stable Processes
- Composite likelihood estimation for the Brown-Resnick process
- Extremal behaviour of aggregated data with an application to downscaling
- An introduction to statistical modeling of extreme values
- Fast Calibrated Additive Quantile Regression
