7 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…
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…