PhD Studentship - Dependence Modelling using Truncated Vine Copulas with Applications (NIKOLOULOPOULOS_U27EMPSFP)

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. 

References

 i) Nikoloulopoulos, A.K. (2025) Vine copula mixed models for meta-analysis of diagnostic accuracy studies without a gold standard. Biometrics, 81(2), ujaf037.

 ii) Kadhem, S.H. and Nikoloulopoulos, A.K. (2023) Factor tree copula models for item response data. Psychometrika, 88:776--802.

 iii) Kadhem, S.H. and Nikoloulopoulos, A.K. (2023) Bi-factor and second-order copula models for item response data. Psychometrika, 88:132--157. 

 iv) Nikoloulopoulos, A.K. (2022) An one-factor copula mixed model for joint meta-analysis of multiple diagnostic tests. Journal of the Royal Statistical Society: Series A (Statistics in Society), 185:1398--1423.

 v) Joe, H., Li, H. and Nikoloulopoulos, A.K. (2010) Tail dependence functions and vine copulas. Journal of Multivariate Analysis, 101:252--270.

Entry Requirements

The entry requirements are either a 1st in your Bachelor's degree or a Master's in Mathematics, Statistics, or Actuarial Science. 

Start date: 1^st 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.

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