Statistics Research Activities

The main interests of this group are multivariate nonparametric statistics, nonparametric smoothing and wavelet based methods, time series analysis, mutual information, statistical computing, applications in bioinformatics and neuroscience.  The group also has strong inter-disciplinary research links with other internationally acclaimed research groups.

The research interests of the members of the group are given below.

Dr Richard Riley

Senior Lecturer in Medical Statistics

Richard specialises in the application and development of statistical methods for evidence synthesis and meta-analysis. His main methodological research interests include:

  • Statistical models for multivariate meta-analysis of multiple outcomes
  • Statistical methods for undertaking an individual participant data (IPD) meta-analysis
  • Approaches to combining IPD with aggregate data in meta-analysis
  • Investigating and dealing with publication and availability bias in IPD meta-analysis.

Particular clinical applications of interest (both in primary studies and systematic reviews) include:

  • Identifying and evaluating diagnostic tests
  • Identifying and evaluating prognostic factors and biomarkers
  • Developing, validating and assessing the impact of prognostic models and risk prediction models
  • Facilitating stratified medicine, in particular by identifying patient-level factors that interact with treatment effect (‘predictive markers’, ‘treatment-covariate interactions’)

A particular research passion is to improve the quality, design, conduct, analysis and reporting of prognosis research studies.

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Dr Biman Chakraborty

Lecturer in Statistics

Biman Chakraborty is currently working on several descriptive statistics tool for the multivariate data using nonparametric rank based and data-depth based methods. He is also working on some new proposals on multivariate control charts. His other interests and ongoing work includes multivariate risk measures and statistical computing.

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Dr Hui Li

Lecturer in Statistics & Econometrics

Environmental & Natural Resource Economics
Applied Econometrics

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