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Join date : 2009-10-21 
Fully Funded Studentship: Dependence modelling with copulas
Thu 16 Feb 2012 - 10:59
Fully Funded Studentship: Dependence modelling with copulas
School: Computing Sciences
Primary Supervisor: Dr Aristidis K. Nikoloulopoulos
Project Description:
This project will focus on dependence modelling 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. Our goal is to develop parametric families of copulas, see for example Nikoloulopoulos and Karlis (2009); Joe et al. (2010); Nikoloulopoulos et al. (2009, 2011b), that are appropriate as models for multivariate data and inferential methods, see for example Nikoloulopoulos et al. (2011a), 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 Chaganty, N. R. (2011a). Weighted scores method for regression models with dependent data. Biostatistics, 12:653-665.
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. (2011b). 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.
Funding Status:
Directly Funded Project (UK Students Only)
Directly Funded Project (European Students Only)
Source of Funding: Funding is available for UK/EU students. Funding awarded for this project 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.
Application Deadline: 30 March 2012
Research Themes:
Data Mining, Machine Learning and Statistics
Acceptable First Degree: At least a 2.1 or equivalent in Statistics/Mathematics or a related discipline.
Keywords:
Data Analysis
Statistics
http://ueasciweb.uea.ac.uk/Resproject/show.aspx?ID=156
School: Computing Sciences
Primary Supervisor: Dr Aristidis K. Nikoloulopoulos
Project Description:
This project will focus on dependence modelling 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. Our goal is to develop parametric families of copulas, see for example Nikoloulopoulos and Karlis (2009); Joe et al. (2010); Nikoloulopoulos et al. (2009, 2011b), that are appropriate as models for multivariate data and inferential methods, see for example Nikoloulopoulos et al. (2011a), 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 Chaganty, N. R. (2011a). Weighted scores method for regression models with dependent data. Biostatistics, 12:653-665.
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. (2011b). 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.
Funding Status:
Directly Funded Project (UK Students Only)
Directly Funded Project (European Students Only)
Source of Funding: Funding is available for UK/EU students. Funding awarded for this project 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.
Application Deadline: 30 March 2012
Research Themes:
Data Mining, Machine Learning and Statistics
Acceptable First Degree: At least a 2.1 or equivalent in Statistics/Mathematics or a related discipline.
Keywords:
Data Analysis
Statistics
http://ueasciweb.uea.ac.uk/Resproject/show.aspx?ID=156
- Fully Funded Studentship: Dependence modelling and construction of multivariate copulas
- Fully Funded Studentship: Dependence modeling with copulas
- 10 fully-funded studentships at the University of Lancaster
- JOB: 3 fully funded PhD positions in Biostatistics in Switzerland
- Five fully funded PhD fellowships in STATISTICS at the University of Milan Bicocca
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