7 papers
Separation-based causal discovery for extremes
Junshu Jiang, Jordan Richards, Raphaël Huser +1
Structural causal models (SCMs), with an underlying directed acyclic graph (DAG), provide a powerful analytical framework to describe the interaction mechanisms in large-scale comp…
Modeling Nonstationary Extremal Dependence via Deep Spatial Deformations
Xuanjie Shao, Jordan Richards, Raphael Huser
Modeling nonstationarity that often prevails in extremal dependence of spatial data can be challenging, and typically requires bespoke or complex spatial models that are difficult…
Quantile-based causal inference for spatio-temporal processes: Assessing the impacts of wildfires on US air quality
Zipei Geng, Jordan Richards, Raphael Huser +1
Wildfires pose an increasingly severe threat to air quality, yet quantifying their causal impact remains challenging due to unmeasured meteorological and geographic confounders. Mo…
Canonical Tail Dependence for Soft Extremal Clustering of Multichannel Brain Signals
Mara Sherlin Talento, Jordan Richards, Raphael Huser +1
We develop a novel characterization of extremal dependence between two cortical regions of the brain when its signals display extremely large amplitudes. We show that connectivity…
The Efficient Tail Hypothesis: An Extreme Value Perspective on Market Efficiency
Junshu Jiang, Jordan Richards, Raphaël Huser +1
In econometrics, the Efficient Market Hypothesis posits that asset prices reflect all available information in the market. Several empirical investigations show that market efficie…
Spectral Extremal Connectivity of Two-State Seizure Brain Waves
Mara Sherlin D. Talento, Jordan Richards, Marco Pinto-Orellana +2
Coherence analysis plays a vital role in the study of functional brain connectivity. However, coherence captures only linear spectral associations, and thus can produce misleading…