Dr Alex Gibberd

Lecturer in Statistics

Research Interests


Theoretical aspects of my research relate to the specification and estimation of stochastic processes. Novel aspects of this work revolve around both issues of non-stationarity, and/or high-dimensionality.

I am especially interested in the role of model-selection in increasing the efficiency of statistical estimation in the presence of structural assumptions, e.g. sparsity. Modern M-estimation frameworks allow an interesting connection to Bayesian prior specification, whilst enabling efficient computational approaches to perform point estimation. I am interested in both how optimisation (computational) and statistical (estimator) related errors affect our ability to recover model structure.


I am interested in data-rich domains which can take advantage of advances in high-dimensional time-series methodology. This may be in either a descriptive (i.e. clustering), or predictive setting. Generally, applications constitute the analysis of complex systems, for instance the brain, via monitoring large numbers of data-streams.