Statistics
This group develops statistical methodology motivated by practical problems, primarily from the bio-medical field. In particular, we apply and develop methodology for the design and analysis of important data sets from (bio-medical) researchers, the industry and government.
Research topics
- Causal inference: evaluating the impact of exposures and interventions on the basis of randomised experiments and observational data.
- High-dimensional data analysis: ensuring valid inference when controlling for large numbers of variables.
- Missing data: minimising the information loss from incomplete data, and eliminating bias due to selective missingness.
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Flexible modeling: finding the best-fitting distribution for complex data sets, under the requirement that the distribution is versatile, parameter-parsimonious and easily interpretable.
- Stein's Method in statistics: uncovering utterly new statistical methods and insights into statistical concepts via this powerful tool from probability theory, with particular focus on Bayesian statistics, asymptotic distribution of estimators, goodness-of-fit tests.
- Sports statistics: developing improved sport rankings based on sound statistical methods, and devising visualization and data analysis methods for the growing field of sports analytics.
- Statistical genetics
- Survival analysis