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PhD funding opportunities based in the department of Statistics of Athens University of Economics and Business (AUEB)

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PhD funding opportunities based in the department of Statistics of Athens University of Economics and Business (AUEB)

Δημοσίευση από grstats Την / Το Κυρ 13 Μαϊος 2012 - 16:20

Applications are invited for several PhD funding opportunities based in the department of Statistics of Athens University of Economics and Business (AUEB) in Greece, to commence in September 2012. The research theme of these grants is based on the project "Likelihood methods for jump diffusions and related Markov processes" that was recently funded in the "ARISTEIA" national funding program. The research team that will act as supervisors consists of Petros Dellaportas (AUEB), Omiros Papaspiliopoulos (Universitat Pompeu Fabra, Barcelona, Spain), Aleksandar Mijiatovic (Imperial College, UK), and Gareth Roberts (Univesity of Warwick, UK).

Candidates should have an MSc degree in statistics, mathematics, computer science or related subjects. Successful candidates will enroll as full-time PhD students in AUEB but will be expected to make several academic visits to other members of the research team.

Detailed cv's naming two academic Referees should be sent to Petros Dellaportas (petros@aueb.gr) by 15/6/2012.

A summary of the research project is given below:
This project is about the development of novel methodology, theory and software for the statistical estimation of a very large family of stochastic models which are widely used in Science. Two different but related descriptions of the family of mathematical models of interest are as solutions to stochastic differential equations driven by Brownian noise and jumps, and as Feller processes. Our target is the identification of such dynamics from observations using likelihood methods. Statistical inference for such processes is well-recognized as a very challenging problem due to the continuous-time non-linear nature of the models, and the discrete-time available data. Specifically, the required likelihood functions are
typically intractable for discrete-time data. This project will attack this problem from two directions. First, by developing
state-of-the-art Markov chain Monte Carlo computational methods for Bayesian inference for these models. Second, by developing tailored approximate likelihood methods for Feller processes by constructing a sequence of approximating but numerically tractable Feller processes. The project aspires to address a broad and ambitious research agenda which includes the underlying probability theory, the computational statistics methodology and the computer implementation.
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