Our main research areas include:
- High-dimensional statistical inference
- Classification, clustering and regression problems
- Data perturbation methods (e.g. subsampling, bootstrap sampling, random projections, knockoffs)
- Time series analysis
- Functional data analysis
- Causal inference
- Large-scale data analysis
- Nonparametric statistics, e.g. asymptotic theory
- Shape-constrained estimation problems
- Changepoint detection and estimation
- Unconditional and conditional independence testing
- Random matrix theory
- Image Analysis
- Spatial-temporal Statistics
- Applications, including genetics, archaeology and oceanography
But we are also able to answer general inquiries from (but not limited to) the following areas:
- Linear models
- Generalised linear models
- Mixed effect models
- Hypothesis testing
- Missing data
- Model fitting and selection
- Monte Carlo methods
- Data mining
- Biostatistics, especially survival analysis
- Bayesian inference
- Experimental design
- Convex optimisation
- Statistical Computing with R
- ...