Research in Statistics
Work in statistics at the University of Kent can be divided into six broad themes which cover both methodological and applied work. The themes are:
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, economics, finance and engineering.
Research in this theme 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.
As the environment changes we see corresponding effects in the behaviour of wild animals and plants, and the Statistical Ecology group at Kent (SE@K) analyse ecological data to try and describe, and better understand, these changes. We were among the founding members of the National Centre for Statistical Ecology (NCSE), which was established in 2005.
Economics and finance
At Kent there is particular interest in the use of nonparametric methods including quantile regression and Bayesian nonparametric approaches. Application areas include modelling of business cycle and capacity utilization, calculating sovereign credit ratings, modelling of stock return data, and predicting inflation.
Multivariate statistics and regression
This theme encompasses both theory and applications. Theory is involved with new models and their analysis by classical, likelihood and Bayesian methodologies. 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.
In the 2014 Research Excellence Framework (REF) 72% of the School's research output was assessed as International Quality or above. The School is in the top 25 in the UK when considered on weighted GPA.
Collaborative and interdisciplinary research were particular strengths.