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Statistical Science

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Research

Research in Statistical Science is based on a blend of project-based research groups, multidisciplinary collaborations and individual research programmes.

Research Themes

The department's methodological research is organised into five areas:

  • Biostatistics. This theme has a research programme that encompasses both applied health research and the development and evaluation of statistical methods.
  • Computational Statistics. This theme is concerned with advancing the theory, methodology, algorithmic development and application of simulation based approaches, such as Markov Chain Monte Carlo, to statistical inference.
  • General Theory and Methodology. The research carried out under this theme covers foundational and theoretical aspects of probability and inferential statistics, and generic statistical methodology.
  • Multivariate and High Dimensional Data. This theme has a research programme that encompasses both the theoretical and methodological problems encountered when analysing multivariate and high dimensional data.
  • Stochastic Modelling and Time Series. This theme covers the development of generic stochastic models and the investigation of their properties, as well as modelling and inference for applications in a range of physical and biological sciences.

Much of this work is interdisciplinary and involves collaborations within and outside UCL.

Research Groups

  • Statistics for Health Economics Evaluation. The activity of this group revolves around the development and application of Bayesian statistical methodology for health economic evaluation, e.g. cost-effectiveness or cost-utility analysis.
  • Statistics in Sports and Health. This group encompasses both methodological and applied research activity for the development and use of statistical methods in sports and health applications.
  • Stochastic Processes Group. This group studies the theory, methodology and application of stochastic processes to dependent data, with particular focus in the areas of time series analysis, spatial statistics and network theory.