Hi Aris,
sorry but it is not possible to post about the opening of positions
(see guidelines in https://sites.google.com/site/deppapersevents/ ;
<https://sites.google.com/site/deppapersevents/> )
The post will be removed.
Best Giovanni
—
Giovanni Puccetti
https://sites.google.com/site/giovannipuccetti/
—
Associate Professor
Department of Economics, Management and Quantitative Methods
University of Milano
Via Conservatorio 7, 20122 Milano (MI) ITALY
—
Journal Editor
Dependence Modeling (De Gruyter Open)
http://www.degruyter.com/view/j/demo
On 04 Oct 2016, at 13:01, Aristidis K Nikoloulopoulos (CMP)
<A.Nikoloulopoulos@xxxxxxxxx> wrote:
PhD Studentship in Statistics
<https://www.uea.ac.uk/study/-/dependence-modelling-using-copulas-with-applications-nikoloulopoulos_u17sci->
| School of Computing Sciences | University of East Anglia | Supervisor: Dr
Aristidis K. Nikoloulopoulos
<https://www.uea.ac.uk/computing/people/profile/a-nikoloulopoulos>
Deadline:
01/12/2016 (Interviews will take place between 16 January and 24 February
2017).
Funding Status: Competition Funded Project (EU Students Only)
This PhD project is in a Faculty of Science competition for funded
studentships. These studentships are funded for 3 years and comprise home/EU
fees, an annual stipend of £14,296 and £1000 per annum to support research
training. Overseas applicants may apply but they are required to fund the
difference between home/EU and overseas tuition fees.
Project description
Multivariate response data abound in many application areas including
insurance, risk management, finance, biology, psychometrics, health and
environmental sciences. Studying associations among multivariate response
data is an interesting problem in statistical science. The dependence
between random variables is completely described by their multivariate
distribution. When the multivariate distribution has a simple form, standard
methods can be used to make inference. On the other hand one may create
multivariate distributions based on particular assumptions, limiting thus
their use. For example, most existing models assume rigid margins of the same
form (e.g., Gaussian, Student, exponential, Gamma, Poisson, etc.) and/or
limited dependence structure.
To solve this problem copulas seem to be a promising solution. Copulas are a
useful way to model multivariate response data, as they account for the
dependence structure and provide a flexible representation of the
multivariate distribution. The power of copulas for dependence modeling is
due to the dependence structure being considered separate from the univariate
margins. They allow for flexible dependence modelling, different from
assuming simple linear correlation structures and normality; see e.g. Joe
(2014). That makes them particularly well suited to many applications in
finance, insurance, medicine and psychometrics, among others.
The PhD project will focus on dependence modelling with copulas for
non-normal multivariate/longitudinal response data and deal with the
development of copula-based,
(a) models with some desirable properties such in Nikoloulopoulos and Joe
(2015) and Nikoloulopoulos (2015),
(b) computationally intensive yet tractable estimation methods such in
Nikoloulopoulos (2016a,2016b),
with applications in biostatistics, psychometrics, insurance, etc.
References
Joe, H. (2014). Dependence Modeling with Copulas. Chapman & Hall, London.
Nikoloulopoulos, A. K. and Joe, H. (2015). Factor copula models for item
response data. Psychometrika, 80:126–150.
Nikoloulopoulos, A. K. (2015) A vine copula mixed effect model for trivariate
meta-analysis of diagnostic test accuracy studies accounting for disease
prevalence. Statistical Methods in Medical Research. DOI: 10.1177/
0962280215596769.
Nikoloulopoulos, A. K. (2016a) Efficient estimation of high-dimensional
multivariate normal copula models with discrete spatial responses. Stochastic
Environmental Research and Risk Assessment, 30:493--505.
Nikoloulopoulos, A.K. (2016b) Correlation structure and variable selection
in generalized estimating equations via composite likelihood information
criteria. Statistics in Medicine, 35:2377--2390.
Apply Online <https://www.uea.ac.uk/study/postgraduate/apply>