A1097
Title: Flexible max-stable processes for fast and efficient inference
Authors: Peng Zhong - University of New South Wales (Australia) [presenting]
Scott Sisson - University of New South Wales (Austria)
Boris Beranger - University of New South Wales (Australia)
Abstract: Max-stable processes serve as the fundamental distributional family in extreme value theory. However, likelihood-based inference methods for max-stable processes still heavily rely on composite likelihoods, rendering them intractable in high dimensions due to their intractable densities. A fast and efficient inference method is introduced for max-stable processes based on their angular densities for a class of max-stable processes whose angular densities do not put mass on the boundary space of the simplex, which can be used to construct r-Pareto processes. The efficiency of the proposed method is demonstrated through two new max-stable processes, the truncated extremal-t process and the skewed Brown-Resnick process. The proposed method is shown to be computationally efficient and can be applied to large datasets. Furthermore, the skewed Brown-Resnick process contains the popular Brown-Resnick model as a special case and possesses nonstationary extremal dependence structures. The new max-stable processes are showcased on simulated and real data.
