Skip to main content

Applied Statistics – University of Reading

Show access keys
  • Research Groups

Applied Statistics

Applied statistics has a rich history at Reading: numerous methodological developments by researchers in this group have given rise to important impacts in a wide range of applications.

Our current research continues in this tradition, with the development of new statistical methods for analysis of large and complex data sets being increasingly important in the current era of 'big data'.

 

Research

Current research interests are predominantly aligned to four overlapping areas.

Medical Statistics

appl_stats_research_medical_statsIn recent years there has been considerable interest in accelerating and refining the drug development process. Statistical innovations in trial design have led to the availability of new approaches to address these objectives. Our group at Reading is primarily involved in the development of methodology within the fields of adaptive designs, in particular adaptive seamless designs & group-sequential trials, pharmacogenetics and pharmacokinetics.

We have strong research links with other academic groups in this field including those based at the Universities of Warwick, Sheffield and Leeds. We also collaborate with both the pharmaceutical industry and public sector institutions. Within our group, several PhD students are studying for the qualification part-time whilst working in the Industry.

People: Sue Todd, Fazil Baksh

Statistical Genetics

appl_stats_research_geneticsRecent developments in whole genome sequencing have led to an explosion in the availability of genetic data. The main challenge is now in drawing inferences from these data. Our group develops methods for achieving this, and uses such methods in applied studies. Areas of particular interests are genome-wide association studies (in both humans and bacteria), family and population genetics for whole genome data, and inference for coalescent models and pathogenomics.

We collaborate with the Modernising Medical Microbiology consortium and East Malling Research, the Infectious Disease Research Centre at Massey University New Zealand, along with the Microbiology, Evolutionary Biology and Crop Research groups at Reading.

People: Fazil Baksh, Richard Everitt

Bayesian Computation

appl_stats_research_bayesianThe Bayesian approach to statistical inference has seen major successes in the past twenty years, finding application throughout science, engineering, finance and other disciplines. The main driver of these successes was the development of Monte Carlo computational methods to perform statistical inference. Our group at Reading is playing an active part in the development of next-generation Monte Carlo techniques, to exploit the increasing size and complexity of modern data sets.

Particular areas of expertise are approximate Bayesian computation, sequential Monte Carlo, and approximate and adaptive MCMC methods. Recent applications include genetics, neuroscience, signal processing and network analysis.

We have a regular Reading Group in Bayesian Computation, also involving members of the Data Assimilation, Polymer Physics, Ecology and Evolutionary Biology groups. This is a part of a university-wide network, the 'Bayes Group', of researchers interested in the use of Bayesian statistics.

People: Richard Everitt

Statistics of Extremes

A graphExtreme value theory provides a rigorous and prolific framework for analysing rare events with severe impact. The development of extremes over space and/or time rests on the celebrated max-stable processes. A recent offspring of extreme value theory is the generalised Pareto process.

Our group has recognised contributions in developing statistical tools for rare events with application to environmental data. We have also established strong international ties with other academics in the field.

People: Claudia Neves

People

Name Position Telephone
+44 (0) 118 378
Email
@reading.ac.uk
Professor Sue Todd Professor of Medical Statistics 8917 s.c.todd
Dr Fazil Baksh Lecturer 8034 m.f.baksh
Dr Richard Everitt Associate Professor 8030 r.g.everitt
Dr Claudia Neves Lecturer 7931 c.neves

Emeritus Professor

Professor Robert N Curnow

Seminars and events

Our lively research environment is supported by a statistics seminar series and research workshops (with strong links to the Probability and Stochastic Analysis theme), together with a strong presence in the Royal Statistical Society local group.

Latest Publications

Jump to: 2017 | 2016
Number of items at this level: 10.

2017

Dimier, N. and Todd, S. (2017) An investigation into the two-stage meta-analytic copula modelling approach for evaluating time-to-event surrogate endpoints which comprise of one or more events of interest. Pharmaceutical Statistics, 16 (5). pp. 322-333. ISSN 1539-1612 doi: 10.1002/pst.1812

Everitt, R. G. (2017) Efficient importance sampling in low dimensions using affine arithmetic. Computational Statistics. ISSN 1613-9658 doi: 10.1007/s00180-017-0729-z

Everitt, R. G., Johansen, A. M., Rowing, E. and Evdemon-Hogan, M. (2017) Bayesian model comparison with un-normalised likelihoods. Statistics and Computing, 27 (2). pp. 403-422. ISSN 1573-1375 doi: 10.1007/s11222-016-9629-2

Kunz, C. U., Stallard, N., Parsons, N., Todd, S. and Friede, T. (2017) Blinded versus unblinded estimation of a correlation coefficient to inform interim design adaptations. Biometrical Journal, 59 (2). pp. 344-357. ISSN 0323-3847 doi: 10.1002/bimj.201500233

Prangle, D., Everitt, R. G. and Kypraios, T. (2017) A rare event approach to high-dimensional Approximate Bayesian computation. Statistics and Computing. ISSN 1573-1375 doi: 10.1007/s11222-017-9764-4

Wollstadt, P., Sellers, K. K., Rudelt, L., Priesemann, V. , Hutt, A., Fröhlich, F. and Wibral, M. (2017) Breakdown of local information processing may underlie isoflurane anesthesia effects. PLoS Computational Biology, 13 (6). e1005511. ISSN 1553-734X doi: 10.1371/journal.pcbi.1005511

2016

Alquier, P., Friel, N., Everitt, R. and Boland, A. (2016) Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels. Statistics and Computing, 26 (1). pp. 29-47. ISSN 1573-1375 doi: 10.1007/s11222-014-9521-x

Derroire, G., Coe, R. and Healey, J. R. (2016) Isolated trees as nuclei of regeneration in tropical pastures: testing the importance of niche-based and landscape factors. Journal of Vegetation Science, 27 (4). pp. 679-691. ISSN 1654-1103 doi: 10.1111/jvs.12404

Howard, D. R., Brown, J. M., Todd, S. and Gregory, W. M. (2016) Recommendations on multiple testing adjustment in multi-arm trials with a shared control group. Statistical Methods in Medical Research. ISSN 0962-2802 doi: 10.1177/0962280216664759

von der Heydt, A. S., Dijkstra, H. A., van de Wal, R. S. W., Caballero, R., Crucifix, M., Foster, G. L., Huber, M., Köhler, P., Rohling, E., Valdes, P. J., Ashwin, P., Bathiany, S., Berends, T., van Bree, L. G. J., Ditlevsen, P., Ghil, M., Haywood, A. M., Katzav, J., Lohmann, G., Lohmann, J., Lucarini, V., Marzocchi, A., Pälike, H., Baroni, I. R., Simon, D., Sluijs, A., Stap, L. B., Tantet, A., Viebahn, J. and Ziegler, M. (2016) Lessons on climate sensitivity from past climate changes. Current Climate Change Reports, 2 (4). pp. 148-158. ISSN 2198-6061 doi: 10.1007/s40641-016-0049-3

This list was generated on Tue Oct 17 07:54:12 2017 BST.

View all publications

We use Javascript to improve your experience on reading.ac.uk, but it looks like yours is turned off. Everything will still work, but it is even more beautiful with Javascript in action. Find out more about why and how to turn it back on here.
We also use cookies to improve your time on the site, for more information please see our cookie policy.

Back to top