Dependence modelling using truncated vine copulas with applications (NIKOLOULOPOULOSA_U26EMP)
Key Details
- Application deadline
- 31 January 2026 for International, 31 March 2026 for Home
- Location
- UEA
- Funding type
- Self-funded
- Start date
- 1 June 2026
- Mode of study
- Full-time
- Programme type
- PhD
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Project description
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 project requires a 1st class degree in Mathematics, Statistics, Actuarial Science.
Funding
This project is offered on a self-funding basis. It is open to applicants with funding or those applying to funding sources. Details of tuition fees can be found here.
A bench fee is also payable in addition to the tuition fee to cover specialist equipment or laboratory costs required for the research. Applicants should contact the primary supervisor for further information about the fee associated with the project.
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References
Nikoloulopoulos, A.K. (2025) Vine copula mixed models for meta-analysis of diagnostic accuracy studies without a gold standard. Biometrics, 81(2), ujaf037.
Kadhem, S.H. and Nikoloulopoulos, A.K. (2023) Factor tree copula models for item response data. Psychometrika, 88:776--802.
Kadhem, S.H. and Nikoloulopoulos, A.K. (2023) Bi-factor and second-order copula models for item response data. Psychometrika, 88:132--157.
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.
Joe, H., Li, H. and Nikoloulopoulos, A.K. (2010) Tail dependence functions and vine copulas. Journal of Multivariate Analysis, 101:252--270.