The research in statistics is in the areas of Bayesian statistics and experimental design. The Bayesian statistics research focuses on Bayesian diagnostics and classification. Particular emphasis and attention is given to Bayes factors for normality diagnostics and Bayesian classification based on conditional predictive ordinates. Recent research has investigated the effect, on a Bayes factor, of omitting observations in time series models.

The research in experimental design is related to the problem of observation loss during experimentation, which can lead to the eventual experimental design being quite different from the design that was selected originally. If the eventual design is disconnected with respect to a class of treatments then linear unbiased estimates of some treatment comparisons will not exist and the experimental objectives are seriously compromised. Research in this area has centred on the identification of rank reducing observation sets for a large class of designs and on specification of staircase structures which summarize the connectivity properties for row-column designs. Pilot procedures have been developed for use at the planning stage to assess the vulnerability of a design to become disconnected. Cross-over designs, which are widely used in pharmaceutical experiments and clinical trials, and factorial designs, which are used extensively in industrial experimentation, are amongst the design types which have been investigated. More details, including staff members working in this area, can be found on the statistics pages.

Below are some examples of PhD projects in statistics. Alternative projects might be formulated following discussions with individual staff members, just contact the staff member or PhD admissions tutor Dr Gianne Derks.