prospectus

Admissions T: +44 (0)1227 827272 www.kent.ac.uk/pg
237
• Modelling of Time-dependent
Data and Financial
Econometrics
• Practical Statistics and
Computing
• Probability and Classical
Inference
• Three from: Analysis of Large
Datasets; Mathematics of
Financial Derivatives; Portfolio
Theory and Asset Pricing
Models; Stochastic Processes
• Project of 12,000 words
Industrial placement
Competition for student
employment remains fierce, so
by combining your postgraduate
degree with relevant employment
experience in a full-time salaried
placement provides you with a
real competitive advantage.
Work placements give you the
opportunity to put theory into
practice, as well as make a
valuable contribution to an
organisation or financial company.
Research programmes
For the most up-to-date information
see www.kent.ac.uk/pg/169
Statistics MSc, MPhil, PhD
www.kent.ac.uk/pg/169
Staff research interests are
diverse, and include: Bayesian
statistics; bioinformatics; biometry;
ecological statistics; medical
statistics; nonparametric statistics
and semi-parametric modelling;
neuro imaging; time series
modelling; high-dimensional
regression; shape statistics.
Statistics has strong connections
with a number of prestigious
research universities such as
Texas A&M University, the
University of Texas, the University
of Otago, the University of Sydney
and other research institutions at
home and abroad.
The research interests of
the group are in line with the
mainstream of statistics, with
emphasis on both theoretical
and applied subjects.
Research areas
Ecology
There has been research in the
area of statistical ecology at Kent
for many years. We are part of the
National Centre for Statistical
Ecology (NCSE), which was
established in 2005. For details
of the work of the NCSE, see
www.ncse.org.uk/
Bayesian statistics
Bayesian statistics is a subset of
the field of statistics where some
initial belief is expressed in terms
of a statistical distribution. The
research conducted in this area
at Kent is mainly on Bayesian
variable selection, Bayesian model
fitting, Bayesian nonparametric
methods, Monte Carlo Markov
chain methods, and applications
in areas including biology, finance,
economics and engineering.
Biological applications
Research is focused on statistical
modelling and inference in biology
and genetics with applications in
complex disease studies. Over the
past few decades, large amounts
of complex data have been
produced by high through-put
biotechnologies. The grand
challenges offered to statisticians
include developing scalable
statistical methods for extracting
useful information from the data,
modelling biological systems with
the data, and fostering innovation
in global health research.
Multivariate statistics and
regression
This theme encompasses both
theory and applications. Theory is
involved with new models and their
STAFF PROFILE
Jim Griffin
Professor of Statistics
Professor Griffin’s research
interests include nonparametric
statistics, regression modelling
and time series. His work has
included the development
of statistical models, which have
been applied to diverse areas
such as forecasting inflation,
analysing stock prices and
identifying cancer subtypes.
He has extensive experience
of cross-disciplinary research in
the areas of finance, economics
and systems biology. He was
recently part of the £1.4
million EPSRC-funded
project, Advanced Bayesian
Computation for Cross-
Disciplinary Research,
looking at fast methods for
fitting models in astronomy,
economics, machine learning
and systems biology.