Statistics

Research in statistics at Lancaster focuses on the interface of methodology and application. We believe that strong interaction with research users is imperative for developing new statistical ideas that are of practical importance.

This philosophy has led to research that has a substantive influence on the discipline of statistics, for example over 10 RSS read papers in the past two decades, as well as a considerable impact on other scientific disciplines, government bodies and industry. The 2021 QS World University Rankings place Lancaster's Statistics and Operational Research at joint 50th in the world, having held a position in the top 100 since the rankings began in 2011.

Our research is divided into five main research areas, each with its own range of activities. Details of these areas and a flavour of some more specific research topics are given below. We also have active links with the operational research group based in the Management Science Department - including co-running of the STOR-i Centre for Doctoral Training - and the CHICAS group from the Medical School.

  • Bayesian and Computational Statistics

    Most real-life applications of statistics require the use of computational methods. These algorithms are important in many areas of statistics, particularly when we need to average over uncertainty within our statistical models.

  • Changepoints and Time Series

    Time series, i.e. data measured over time, arise in many natural and industrial applications. Being able to analyse the changing structure of such data is key to understanding the dynamics of many important physical processes.

  • Extreme Value Statistics

    Since extreme events are rare by definition, prediction of future events relies on extrapolation from a suitable model fitted to historical data. Extreme value analysis provides a statistical framework for this kind of analysis.

  • Medical and Social Statistics

    The Medical and Social Statistics group specialises in methodology for clinical trials, statistical epidemiology, quantitative criminology and social research.

  • Statistical Learning

    Statistical learning designs and analyses algorithms that learn from data. Usually our focus is on sequential learning, or large scale data, such as that found in analysis of the internet.

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