The Department of Statistics' research is concentrated in three key areas; risk and stochastics; social statistics; time series and statistical learning.
Risk and Stochastics
Our research in risk and stochastics covers diverse aspects in quantitative modelling in finance, insurance, and risk management. Current areas include robust models on option pricing; model-uncertainty in decision making; valuation financial derivatives with exotic features; equilibrium with market constraints and informational asymmetry; optimal trading with micro-structure noise; insurance securitisation; contagion in financial and insurance markets; modelling energy and commodity markets.
Research in social statistics is concerned with the development of statistical methods that can be used across the social sciences. Statisticians play an essential role in all aspects of social inquiry, including: study design; measurement; data linkage; development of statistical models that account for the complex structure of social data; model selection and assessment.
Members of the Social Statistics group have interest in statistical methods in each of these areas and regularly collaborate with social scientists whose questions motivate new lines of methodological research. We have experience in a range of social science disciplines, including demography, education, epidemiology, psychology and sociology, and psychology.
The Department's research in time series and statistical learning encompasses many aspects of the discipline. We are keenly involved in both theoretical developments and practical applications. Current areas of interest include non-parametric inference for financial time series, model error in forecasting non-linear systems, structural modelling of weather series and decision support using weather and climate models.
The Centre for the Analysis of Time Series (CATS) is affiliated with the department. The centre aims to ddress the question of data analysis using both physical insight and the latest statistical methods; focus on non-linear analysis in situations of economic and physical interest, such as weather forecasting; promote awareness of limitations of non-linear analysis and the danger of blindly transferring well-known physics to simulation modelling; focus on end-to-end forecasting, taking account of current uncertainty about the state of the system, model inadequacy and finite computational power.