5 papers · 1 filter
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…
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 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…