Dr Alex Gibberd

Research Interests

Theory/Methodology

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.

Application

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.