Dr Alex GibberdLecturer in Statistics
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.
PhD Supervision Interests
Students interested in the intersection of high-dimensional statistics and time-series analysis.
Model Selection for High-Dimensional Temporal Disaggregation in Official Statistics
01/03/2021 → 31/05/2022
Reducing End Use Energy Demand in Commercial Settings Through Digital Innovation
01/01/2021 → 31/12/2024
Wavelet Methods for Dependency Analysis in Multivariate Time Series (Jessica Green)
29/06/2020 → 21/08/2020
STORi: Information Fusion for Non-homogeneous Panel and Time-series Data
01/10/2019 → 31/03/2023
Support of Collaborative Research with Dr S. Roy at University of Bath
01/01/1900 → …
- Centre for Marketing Analytics & Forecasting
- Changepoints and Time Series
- DSI - Foundations
- STOR-i Centre for Doctoral Training