Department of Mathematical Sciences


Statistical research is the development and application of methods to infer underlying structure from data.

The Bath statistics research group specialises in environmental and epidemiological statistics, sequential clinical trials, Bayesian statistics, smoothing and functional data analysis. We focus on producing models and methods directly useful beyond academic statistics and on theoretical aspects of statistics.

In addition to core statistical research, members of the group undertake consultancy work where this has a substantial innovative component. We also provide advanced ('knowledge transfer') courses on sequential clinical trials and semiparametric regression modelling. Our research is currently funded by EPSRC, NIHR, EC and the Forest Research Institute Baden W├╝rttemberg (Germany).

Our research in statistics can be arranged under five broad headings:

  • Environmental and epidemiological statistics
  • Medical statistics
  • Statistical computing
  • Smoothing and functional data
  • Bayesian methods

Related areas of our research include:

Further details of our research are given below.

Environmental and epidemiological statistics

Research staff: Nicole Augustin, Gavin Shaddick

Gavin Shaddick is interested in all areas of statistical air pollution modelling, from modelling the pollutants themselves through to their effects on human health. Nicole Augustin is interested in environmental statistics, particularly in relation to European forest health and climate change.

Medical statistics

Research staff: Chris Jennison

Chris Jennison researches into the design and analysis of clinical trials. His book with Bruce Turnbull is a key text on the interim monitoring of clinical trials. His current research interests include: adaptive designs that allow treatment selection or enrichment of a subpopulation during a trial; adaptive designs for dose finding studies; optimisation of the combined Phase II / Phase III process.

Statistical computing

Research staff: Julian Faraway, Merrilee Hurn, Chris Jennison, Simon Wood

Merrilee Hurn and Chris Jennison carry out research into innovative MCMC methods. Simon Wood is interested in reliable and efficient computational methods for semi-parametric and mixed models. He is author of the software package mgcv and an accompanying textbook; this package implements numerical methods for semi-parametric modelling within R, the widely-used statistical modelling software. Julian Faraway has written two very successful textbooks on using R for advanced regression modelling.

Smoothing and functional data

Research Staff: Julian Faraway, Simon Wood

Simon Wood and Julian Faraway are interested in various aspects of smoothing and functional data analysis. Simon works on penalised regression spline smoothing and computational methods for estimating penalised GLMs. Julian works on functional data in the context of movement modelling.

Bayesian methods

Research staff: Evangelos Evangelou, Merrilee Hurn, Chris Jennison, Simon Shaw

Bayesian analysis is a popular choice for complex problems with useful prior information and multiple sources of uncertainty. Merrilee Hurn and Chris Jennison have applied Bayesian methods to inverse problems arising in a variety of image analysis applications. Simon Shaw is interested in developing Bayes linear methods, in which expectation rather than probability is taken as primitive for expressing beliefs about random quantities of interest so that only a partial prior specification is required. Particular interests are in Bayes linear kinematics, for synthesising models with probabilistic and Bayes linear components and in applications to software testing. Merrilee Hurn works on Bayes estimates for image analysis and mixture models. The loss function plays a central role here, and she is interested in the case where the most reasonable loss does not admit closed form estimators. Evangelos Evangelou is working on prediction-optimal criteria for prior selection for the covariance parameters of latent Gaussian variables.