extreme statistics

Modeling Complex Dependence in Multivariate Time Series, with Applications to Brain Signals

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This thesis develops approaches to address issues and limitations associated with multivariate processes generated from unknown complex systems such as brain signals.

Understanding Extreme Market Behavior: The Efficient Tail Hypothesis

2 min read ·
A new study by researchers Junshu Jiang, James Richards, Raphaël Huser, and David Bolin introduces the Efficient Tail Hypothesis (ETH), an analogue of the Efficient Market Hypothesis focused on extreme events in financial markets. Drawing on extreme value theory, the team developed a novel statistical measure to evaluate whether asset markets remain informationally efficient even during rare, extreme fluctuations. Their findings not only challenge conventional wisdom on market efficiency but also open the door to identifying potential opportunities (and risks) that emerge during market

Spatial Models and Extreme-Value Methods for Wildfire Risk Assessment

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B3 L5 R5220

The statistical modeling of spatial and extreme events provides a framework for the development of techniques and models to describe natural phenomena in a variety of environmental, geoscience, and climate science applications. In a changing climate, various natural hazards, such as wildfires, are believed to have evolved in frequency, size, and spatial extent, although regional responses may vary.

Modeling the neighborhood boosts landslide prediction

1 min read ·
A prediction model that considers multiple landslides over time in a given region may improve the accuracy of early warning systems.

Star light, star bright…as explained by math

1 min read ·
The evolving periodicity of the brightness of certain types of stars can now be described mathematically.

Competition sheds light on approximation methods for large spatial datasets

1 min read ·
Marc Genton, Huang Huang and colleagues from KAUST organized a global competition with 21 competing teams to compare different approximation methods for analyzing large spatial datasets.

Paolo Victor Redondo

Ph.D. Student, Statistics
Ph.D. Student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser and Prof. Hernando Ombao.