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Details of Grant 

EPSRC Reference: EP/P002838/1
Title: Methodology for High-Dimensional Multivariate Extremes
Principal Investigator: Wadsworth, Dr J
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Mathematics and Statistics
Organisation: Lancaster University
Scheme: EPSRC Fellowship
Starts: 01 October 2016 Ends: 30 September 2019 Value (£): 239,347
EPSRC Research Topic Classifications:
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
19 Jul 2016 EPSRC Mathematical Sciences Fellowship Interviews July 2016 Announced
08 Jun 2016 EPSRC Mathematics Prioritisation Panel Meeting June 2016 Announced
Summary on Grant Application Form
Most people accept that there are risks in our day-to-day lives. However, we also expect that these risks are managed so that the probability of catastrophe is acceptably low, without infringing on our ability to get on with daily life. For example, we could perhaps eliminate flooding by building very high flood defences on all riverbanks, but we choose not to because this would be disproportionate to the risk: diverting money from other necessary services, and creating other inconveniences.

In order to manage the risk proportionately, we need to be well informed about the probability of such rare but disastrous events. The question is how can we do this when we may never have witnessed an event of the size we with to protect against? Extreme value theory is the probabilistic theory of rare events, and provides a rational framework for drawing inference about the likelihood of future extremes given data on past extremes. Models for extremes of a single variable (e.g. river flow at a particular gauging station) are relatively well developed. However, most catastrophic events occur when extremes of different variables combine, or aggregate over space. In order to fully understand the risks we therefore need multivariate and spatial models, and in order that these models produce reliable estimates, they should be motivated by extreme value theory.

The multivariate and spatial extreme value models that are commonly used today suffer from restrictive assumptions and / or can only be applied to very low dimensions (e.g. to the joint extremes of two variables). The goal of this project is to build new models for multivariate and spatial extremes that are appropriate under more general assumptions, and to extremes of a greater number of variables, so that they are more applicable to the problems of interest. This will enable better estimation of the probability of extreme events, and thus improve our management of these risks.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Organisation Website: http://www.lancs.ac.uk