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Statistical Methods for Analytical Science

The Department of Statistics at Warwick is a vibrant community of researchers with expertise ranging from probability theory through statistical methodology to applications in many areas of science. Facilities for computer-intensive statistical methods are first class, and include a high-performance cluster dedicated to statistical computation.

Centre for Scientific Computing RTP

Researchers in Statistics with interests aligned with WASC activity include:

Dr John Aston - Analysis of images, including Scanning Electron Microscopy images and Magnetic Resonance images. In particular, he has published work on incorporating rigorous statistical methodology for the analysis of time series of images, as well as using statistical techniques to enable resolution recovery.

Professor David Firth - General statistical methodology for scientific research, including:
  • generalized linear nonlinear statistical modelling
  • measurement and missing-data problems
  • handling complex error structures
  • optimum design of experiments
Dr Adam Johansen - Interests include integral equations and inverse problems and inferential methodology. Previous experience includes microscopy and image analysis.
Professor Wilfrid Kendall - Is interested in construction of applied probability models to be of service in problems involving interactions between probability and geometry. For example:
  • the use of Ising models based on quad-trees in multiresolution image analysis;
  • application of models for random lines in construction of efficient networks;
  • inference of fibre structure from associated point sets.
Dr Thomas Nichols - Modelling & Inference for neuroimaging data. Specific projects include:
  • multiple testing procedures that are sensitive to spatial signals while strictly controlling false positive risk
  • point process models that account intersubject heterogeneity in location of signal
  • meta-analysis for data taking the form of a spatial point process
  • Bayesian statistics
  • Inference for stochastic processes
  • Network inference for protein microarray data
  • Bayesian model selection and model averaging
  • Stochastic processes and applied probability
  • Stochastic epidemic models
  • Outbreak detection methods


MSc/Diploma in Statistics

CH923 AS:MIT Module Statistics for Data Analysis

Centre for Research in Statistical Methodology (CRiSM) Workshop Programme