Ticknor, B. (STAT) – Clustering and Tractable Multivariate Inference for Extremes

Modeling environmental extremes often involves large collections of spatial or temporal records where both clustering similar series and modeling dependence among extremes are challenging tasks. This Ph.D. proposal addresses several related problems in extreme value analysis. In particular, we study how to cluster many time series based on their extremal behavior using strategies defined via univariate extremal models, motivated by an application to 975 coastal wave-height records. We also investigate the development of scalable multivariate models for dependent extremes. A tractable construction based on a latent multivariate $t$ process with generalized extreme value margins is proposed, together with a regularization strategy that encourages extremal dependence consistent with a max-stable limit while preserving likelihood-based inference. Together, these efforts aim to provide practical tools for analyzing large collections of environmental extremes.

Event Host: Benjamin Ticknor, Ph.D. Student, Statistical Science

Advisor: Robert Lund

Zoom- https://ucsc.zoom.us/j/94347069554?pwd=21jbzUIlbopj2OFRySIHmBV11Ngoef.1

Passcode- 822764