Statistical Computing - 15 credits
The use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems and for analysing very large data sets."Statistical computing" is the branch of mathematics which concerns these techniques for situations which either directly involve randomness, or where randomness is used as part of a mathematical model.
Dissertation in Statistics - 60 credits
Each student will discuss with an individual supervisor a suitable research project. The title and objectives of the project will be approved by the Programme Manager.
Optional modules include
Linear Regression and Robustness - 15 credits
This module will examine ways of predicting one particular variable from the remaining measurements using the linear regression model. The general theory of linear regression models will be covered, including variable selection, tests and diagnostics. Robust methods will be introduced to deal with the presence of outliers.
Multivariate and Cluster Analysis - 15 credits
Multivariate datasets are common to all research areas: it is typical that experimental units are measured for (or questioned about) more than one variable at a time. This module covers the extension of univariate statistical techniques for continuous data to a multivariate setting and introduces methods designed specifically for multivariate data analysis (cluster analysis, principal component analysis, multidimensional scaling and factor analysis).
Stochastic Calculus for Finance - 15 credits
This module provides a mathematical introduction to stochastic calculus in continuous time with applications to finance. Students will learn material in areas of mathematical analysis and probability theory. This knowledge will be used to derive expressions for prices of derivatives in financial markets under uncertainty.
Generalised Linear Models - 15 credits
Linear regression is a tremendously useful statistical technique but is very limited. Generalised linear models extend linear regression in many ways - allowing us to analyse more complex data sets. In this module we will see how to combine continuous and categorical predictors, analyse binomial response data and model count data.
Risk Management - 15 credits
This module covers the different sorts of risk to which financial investments are exposed, basic and sophisticated derivates commonly used for hedging, expected utility theory, models of incomplete markets, Value-at-Risk and other risk measures, credit risks and credit derivatives, methods to determine the effectiveness of a hedge, stress-testing of risky investment portfolios.