Developing clustering algorithms for conditional extremes models

Conditional extreme value models have proven useful for analysing the joint tail behaviour of random vectors. While
an extensive amount of work to estimate conditional extremes models exists in multivariate and spatial applications,
the prospect of clustering for models of this type has not yet been explored. This PhD project aims to develop
clustering algorithms to analyse conditional extremes models in a wide range of settings. These clustering methods
will be designed to aid exploratory analysis and/or parameter estimation. The methods will be implemented to test
their computational cost and performance will be assessed rigorously using simulated and real-world data. Possible
applications include the analysis of several weather variables across multiple spatial sites.