10 papers
Causal Discovery in Multivariate Extremes via Tail Asymmetry
Mengran Li, Daniela Castro-Camilo
Causal discovery in multivariate extremes is challenging because extreme observations are sparse, dependent, and often affected by latent common shocks. Existing approaches focus o…
Tail-Calibrated Estimation of Extreme Quantile Treatment Effects
Mengran Li, Daniela Castro-Camilo
Extreme quantile treatment effects (eQTEs) measure the causal impact of a treatment on the tails of an outcome distribution and are central for studying rare, high-impact events. S…
Probabilistic forecasting of weather-driven faults in electricity networks: a flexible approach for extreme and non-extreme events
Mateus Maia, Daniela Castro-Camilo, Jethro Browell
Electricity networks are vulnerable to weather damage, with severe events often leading to faults and power outages. Timely forecasts of fault occurrences, ranging from nowcasts to…
XGBoost meets INLA: a two-stage spatio-temporal forecasting of wildfires in Portugal
Chenglei Hu, Regina Baltazar Bispo, Håvard Rue +3
Wildfires pose a major threat to Portugal, with over 115,000 hectares burned annually on average during 1980-2024, and the country has faced devastating mega-fires such as those in…
On the importance of tail assumptions in climate extreme event attribution
Mengran Li, Daniela Castro-Camilo
Extreme weather events are becoming more frequent and intense, posing serious threats to human life, biodiversity, and ecosystems. A key objective of extreme event attribution (EEA…
Spatio-temporal fusion of reanalysis and in situ data for censored threshold exceedances of PM2.5
M. Daniela Cuba, Craig Wilkie, Marian Scott +1
Data fusion models are widely used in air quality monitoring to integrate in situ and large-scale gridded products, offering spatially complete and temporally detailed estimates. H…
GPDFlow: Generative Multivariate Threshold Exceedance Modeling via Normalizing Flows
Chenglei Hu, Daniela Castro-Camilo
The multivariate generalized Pareto distribution (mGPD) is a common method for modeling extreme threshold exceedance probabilities in environmental and financial risk management. D…
A bivariate spatial extreme mixture model for unreplicated heavy metal soil contamination
M. Daniela Cuba, Marian Scott, Benjamin P. Marchant +1
Geostatistical models for multivariate applications such as heavy metal soil contamination work under Gaussian assumptions and may result in underestimated extreme values and misle…
A Bayesian multivariate extreme value mixture model
Chenglei Hu, Ben Swallow, Daniela Castro-Camilo
Impact assessment of natural hazards requires the consideration of both extreme and non-extreme events. Extensive research has been conducted on the joint modeling of bulk and tail…
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…