19 citations · 25 across the 11 of their papers we have counts for
12 papers · 1 filter
Modeling spatial extremes using normal mean-variance mixtures
Zhongwei Zhang, Raphaël Huser, Thomas Opitz +1
Classical models for multivariate or spatial extremes are mainly based upon the asymptotically justified max-stable or generalized Pareto processes. These models are suitable when…
Spatiotemporal wildfire modeling through point processes with moderate and extreme marks
Jonathan Koh, François Pimont, Jean-Luc Dupuy +1
Accurate spatiotemporal modeling of conditions leading to moderate and large wildfires provides better understanding of mechanisms driving fire-prone ecosystems and improves risk m…
High-resolution Bayesian mapping of landslide hazard with unobserved trigger event
Thomas Opitz, Haakon Bakka, Raphaël Huser +1
Statistical models for landslide hazard enable mapping of risk factors and landslide occurrence intensity by using geomorphological covariates available at high spatial resolution.…
Modeling Non-Stationary Temperature Maxima Based on Extremal Dependence Changing with Event Magnitude
Peng Zhong, Raphaël Huser, Thomas Opitz
The modeling of spatio-temporal trends in temperature extremes can help better understand the structure and frequency of heatwaves in a changing climate. Here, we study annual temp…
Bayesian space-time gap filling for inference on extreme hot-spots: an application to Red Sea surface temperatures
Daniela Castro-Camilo, Linda Mhalla, Thomas Opitz
We develop a method for probabilistic prediction of extreme value hot-spots in a spatio-temporal framework, tailored to big datasets containing important gaps. In this setting, dir…
Semi-parametric resampling with extremes
Thomas Opitz, Denis Allard, Grégoire Mariéthoz
Nonparametric resampling methods such as Direct Sampling are powerful tools to simulate new datasets preserving important data features such as spatial patterns from observed datas…