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Primary Supervisor: Dr. Aristidis K. Nikoloulopoulos
Multivariate response data abound in many applications including insurance, risk management, finance, psychometrics, health and environmental sciences. Data from these application areas have different dependence structures. While a multivariate distribution fully encodes this dependence, the tractable families used in practice often impose restrictive marginal or dependence structures. Copula functions alleviate these constraints by separating the margins from the dependence structure. Although classical copulas are naturally suited to low-dimensional settings, vine copulas extend the framework to high dimensions. We have shown that a vine copula displays (tail) dependence in all bivariate margins provided that the pair-copulas in the first level possess (tail) dependence; higher-level pair-copulas may be independence copulas without loss of overall (tail) dependence. This insight justifies truncating the vine after the first level, creating a parsimonious model that retains the essential dependence structure. In this project, we will make use of truncated vine copulas with both observed and latent variables in the aforementioned application areas.
Entry Requirements
The entry requirements are either a 1st in your Bachelor’s degree or a Master’s in Mathematics, Statistics, or Actuarial Science.
Mode of study: Full or Part time
Start date: 1st October 2026
Funding:
This project is offered on a self-funded basis. It is open to applicants who are self-funded or who are in the process of securing external funding.
A bench fee is payable in addition to the tuition fee, to cover the cost of specialist equipment and laboratory facilities required for the research. Applicants should contact the primary supervisor for details of the bench fee applicable to this project.
If you are part of the UEA alumni community, you may be eligible for a tuition fee discount.
For information on doctoral funding, visit our Postgraduate Student Loans page.