BIRS Workshop Lecture Videos
and Emeric Thibaud: Extreme value analysis for large spatial data sets Reich, Brian
The extremes community has developed elegant theory for spatial processes and more recently computational methods to handle small- to moderately-sized datasets. In this talk we consider two fully-Bayesian approaches to analyze large datasets covering thousands of spatial locations often seen in modern applications. In the first approach we use data-driven basis functions and a low-rank max-stable process to efficiently represent spatial variability. In the second approach we used skewed-t processes which capture extremal dependence with computation on the order of a Gaussian analysis. These methods are used to study extreme precipitation, air pollution, and forest fire events. This is joint work with Sam Morris (NCSU), Dan Cooley (CSU), and Emeric Thibaud (CSU).
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