8 papers
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…
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…
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…
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…
XGBoost meets INLA: a two-stage spatio-temporal forecasting of wildfires in Portugal
Chenglei Hu, Regina Baltazar Bispo, HÃ¥vard 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…
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…