Statistics
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Statistics is concerned with the analysis and interpretation of data, the design of experiments and decision-making under uncertainty.
Statistics is a foundational information science that offers a systematic and coherent mathematical framework for all questions arising in data analysis. These range from designing experiments and optimally extracting information from observations to predicting new outcomes, uncovering functional relationships and taking decisions. Statistical methods provide theoretical guarantees of optimality and use probability to represent uncertainty in models and data.
Statistics has many facets, ranging from theoretical and mathematical foundations, developing new models and methods, designing algorithms and computer code as well as practical application, e.g. in Biology, Medicine or Finance. Current challenges in Statistics include complex-structured models and data, high-dimensional and nonparametric data inference and developing effective computational tools for large-scale data.
Our research projects
The Statistics group is involved in research in wide-ranging areas of statistical modelling and data analysis, such as:
- Modelling for longitudinal data and survival data
- High-dimensional statistical inference,
- Bayesian statistics and Bayesian data analysis,
- Experimental design,
- Multiple testing,
- Time course data analysis,
- Spatial statistics,
- Advanced regression models,
- Development of statistical software, and
- Analysis of epidemiological, medical, and genomic data.
- Investigation of data and models for quantitative finance.
More information about our research outputs and research-related activities can be found by browsing the webpages of the staff listed on the right. Potential PhD students may email academic staff directly to discuss possible projects.
Connections to Data Science
Statistical methods are essential in Data Science to enable the optimal use of collected data. They also provide the underpinnings of probabilistic tools in Machine Learning and Artificial Intelligence.
Research seminars
Recommended research seminars:
Research staff list
- Madhuchhanda Bhattacharjee
- Georgi Boshnakov
- Christiana Charalambous
- Huy Chau
- Robert Gaunt
- Ian Hall
- Yang Han
- Ines Henriques-Cadby
- Thomas House
- Xiong Jin
- Olatunji Johnson
- Jonas Latz
- Kody Law
- Wentao Li
- Mark Muldoon
- Saralees Nadarajah
- Theodore Papamarkou
- Lorenzo Pellis
- Korbinian Strimmer
- Timothy Waite
- Jingsong Yuan
