24 May 2024 

(13:00 UTC) = (15:00 Paris) = (09:00 New York) = (23:00 Sydney)

Phyllis Wan (Erasmus University, Rotterdam)

A comparative tour of clustering methods for multivariate extremes

Abstract: When dealing with extreme observations of large dimensions, the scarcity of relevant observations in combination with model uncertainty calls for simple but flexible non-parametric learning algorithms for extremal dependence. Recently, clustering algorithms have been engaged in various ways in this task, and the combination of the multivariate regular variation framework with established algorithms to work on the (estimated) spectral measure has led to some effective tools.  In this talk, we compare three algorithms in the literature: i) spherical K-means clustering (Janssen and Wan, 2020); ii) spherical K-principal-components clustering (Fomichov and Ivanov, 2022); iii) spectral clustering (Avella Medina et al., 2023).  We discuss their respective advantages, theoretical results, and implementations in data applications.