Fully Funded Studentship: Dependence modeling with copulas

For more details and on line application see here http://ueasciweb.uea.ac.uk/Resproject/show.aspx?ID=112

University of East Anglia

School: Computing Sciences
Supervisor(s): Dr Aristidis K. Nikoloulopoulos
Application Deadline: November 19th 2010

Funding may be available for UK/EU students. If funding is awarded for this project it will cover tuition fees and stipend for UK students. EU students may be eligible for full funding, or tuition fees only, depending on the funding source. International students will not be eligible for this funding however they are still welcome to apply for this project but would have to find alternative funding.

Project Description:

This project will focus on dependence modeling with copulas for non-normal multivariate/longitudinal response data. Such data abound in many applications including insurance, risk management, finance, biology, health and environmental sciences with different dependence structures including features such as tail dependence or negative dependence. Our goal is to develop parametric families of copulas, see for example Nikoloulopoulos and Karlis (2009); Joe et al. (2010); Nikoloulopoulos et al. (2009, 2011), that are appropriate as models for multivariate data and special estimation methods, see for example Zhao and Joe (2005), to overcome computational complexities imposed by some existing families.

References:

• Joe, H., Li, H., and Nikoloulopoulos, A. K. (2010). Tail dependence functions and vine copulas. Journal of Multivariate Analysis, 101:252-270.
  
  
• Nikoloulopoulos, A. K., Joe, H., and Li, H. (2009). Extreme value properties of multivariate t copulas. Extremes, 12:129-148.
  
  
  
• Nikoloulopoulos, A. K., Joe, H., and Li, H. (2011). Vine copulas with asymmetric tail dependence and applications to financial return data. Computational Statistics & Data Analysis. In press.
  
  
• Nikoloulopoulos, A. K. and Karlis, D. (2009). Finite normal mixture copulas for multivariate discrete data modeling. Journal of Statistical Planning and Inference, 139:3878-3890.
  
  
• Zhao, Y. and Joe, H. (2005). Composite likelihood estimation in multivariate data analysis. The Canadian Journal of Statistics, 33:335-356.
  
  

Funding Status:

• Directly Funded Project (UK Students Only)
  
  
• Directly Funded Project (European Students Only)
  

Research Themes: Data Mining, Machine Learning and Statistics, Applied Mathematics

Acceptable First Degree: Statistics, Mathematics

Keywords: Applied Mathematics, Data Analysis, Statistics