Research interests

I am interested primarily in (optimal) statistical design of experiments, together with associated modelling and uncertainty quantification problems.

In design of experiments, we seek methods and strategies for choosing a good allocation of the limited resources in a physical or computational experiment in order to maximize the ‘information’ gained.

An important consideration is how to quantify the amount of information that will likely be obtained.

I alternate between Bayesian and non-Bayesian, e.g. minimax, approaches. Some particular problems of interest include design for models for grouped discrete data (GLMMs), methodology for Bayesian and pseudo-Bayesian design, computer model calibration, and random design strategies, especially as applied to model-robust design theory.

Grants

Knowledge Transfer Partnership with Arrow Global Ltd (joint with Simon Cotter), 2018-21. £277,674.

Collaborators

Dave Woods, Antony Overstall, Simon Cotter

Postdocs

Ed Ryan (2018-21). KTP with Arrow, investigating Monte Carlo methods for improved collections forecasting.

PhD students

Yiolanda Englezou (2014-18). Decision theoretic Bayesian design of experiments for computer model calibration. University of Southampton, co-supervised with Dave Woods.

Conference, session, and seminar organisation

Organiser of session ‘Advances in Design of Experiments’ at CMStatistics 2022
Organiser of session ‘Bayesian experimental design’ at CMStatistics 2021
Organiser of session ‘Randomization and causal inference’ at ICODOE 2019, Memphis, TN
Organiser of session on ‘Robustness of design’ at ICODOE 2016, Memphis, TN
Co-organiser of the 2015 Bayesian Optimal Design of Experiments meeting in Brisbane, Australia
Co-organiser of the MSG seminar series on Design of Experiments, 2015-
Statistics seminar organiser and Uncertainty Quantification seminar co-organiser, School of Mathematics, University of Manchester, 2015-18

Publications

Baynes, S., Cotter, S., Russell, P., Ryan, E. and Waite, T. (2023) [*accepted 2022] Efficient forecasting and uncertainty quantification for large scale account level Monte Carlo models of debt recovery, Journal of the Royal Statistical Society Series C, 72, 188-212. Paper: https://doi.org/10.1093/jrsssc/qlad008 Code: https://github.com/timwaite/arrow.
Englezou, Y., Waite, T.W. and Woods, D.C. (2022) ‘Approximate Laplace Importance Sampling for the estimation of expected Shannon information gain in high-dimensional Bayesian design for nonlinear models’, Statistics and Computing, 32:82 doi:10.1007/s11222-022-10159-2
Waite, T.W. and Woods, D.C. (2022) [*accepted 2020] ‘Minimax efficient random experimental design strategies with application to model-robust design for prediction’, Journal of the American Statistical Association, 117:539, 1452-1465, doi:10.1080/01621459.2020.1863221.
Waite, T.W. (2018) ‘Singular prior distributions and ill-conditioning in Bayesian D-optimal design for several nonlinear models’, Statistica Sinica, 28, 505-525. ArXiv preprint (inc. supplementary material).
Woods, D.C., Overstall, A.M., Adamou, M. and Waite, T.W. (2017) ‘Bayesian design of experiments for generalized linear models and dimensional analysis with industrial and scientific application’, Quality Engineering, 29, 91-103. doi:10.1080/08982112.2016.1246045.
Waite, T.W. and Woods, D.C. (2015) ‘Designs for generalized linear models with random block effects via information matrix approximations’, Biometrika, 102, 677-694. doi:10.1093/biomet/asv005
Waite, T.W. (2012) Design of experiments with mixed effects and discrete responses plus related topics. PhD thesis, University of Southampton. ePrints.
Waite, T.W. and Woods, D.C. (2012), Comment on paper by Gilmour, S.G. and Trinca, L.A., J. Roy. Statist. Soc. C, 61, pp. 374-5.