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.
  • 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