These programmes offers the opportunity to begin or consolidate your research career under the guidance of internationally renowned researchers and professionals in the School of Mathematics, Statistics and Actuarial Science (SMSAS).
Research interests are diverse and include: Bayesian statistics; bioinformatics; biometry; ecological statistics; epidemic modelling; medical statistics; nonparametric statistics and semi-parametric modelling; risk and queueing theory; shape statistics.
Statistics at Kent provides:
The School has a strong reputation for world-class research and a well-established system of support and training, with a high level of contact between staff and research students. Postgraduate students develop analytical, communication and research skills. Developing computational skills and applying them to mathematical problems forms a significant part of the postgraduate training in the School. We encourage all postgraduate statistics students to take part in statistics seminars and to help in tutorial classes.
The Statistics Group is forward-thinking, with varied research, and received high rankings in the Research Excellence Framework (REF) 2014 for research power and quality.
The research interests of the group are in line with the mainstream of statistics, with emphasis on both theoretical and applied subjects.
There are 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 group regularly receives research grants. The EPSRC has awarded two major grants, which support the National Centre for Statistical Ecology (NCSE), a joint venture between several institutions. A BBSRC grant supports stochastic modelling in bioscience.
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 http://www.ncse.org.uk
The research conducted in this area at Kent is mainly on Bayesian variable selection, Bayesian model fitting, Bayesian nonparametric methods, Markov chain Monte Carlo with 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.
This theme encompasses both theory and applications. Theory is involved with supervised and unsupervised learning, matrix factorisation, modelling of high-dimensional time series, differential privacy, deep learning and networks, shape analysis and statistics on manifolds, and neuroimaging. Applications in biology, industry, medicine and psychiatry. Often new computational methods are the key to analysing complex big data problems.
In order to describe the data, it is common in statistics to assume a specific probability model. Unfortunately, in many practical applications (for instance in economics, population genetics and social networks) it is not possible to identify a specific structure for the data. Nonparametric methods provide statistical tools for addressing inference in these situations.
At Kent there is particular interest in the use of nonparametric methods including quantile regression and Bayesian nonparametric approaches. Application areas include modelling of the business cycle and capacity utilisation, calculating sovereign credit ratings, modelling of stock return data, and predicting inflation.
Kent’s world-class academics provide research students with excellent supervision. The academic staff in this school and their research interests are shown below. You are strongly encouraged to contact the school to discuss your proposed research and potential supervision prior to making an application. Please note, it is possible for students to be supervised by a member of academic staff from any of Kent’s schools, providing their expertise matches your research interests. Use our ‘find a supervisor’ search to search by staff member or keyword.
Lancaster University, Bailrigg, Lancaster, UK
October 01, 2022
Imperial College London
September 01, 2022
King’s College London
King's College Hospital, Denmark Hill, London, UK
October 01, 2022