The MSc in Statistics provides students with intensive training in statistics applicable to the social sciences, econometrics and finance. The aim of the course is to foster an interest in applied statistics and equip students for work as professional statisticians. The MSc also provides an opportunity to study specialist courses in related disciplines. There are excellent prospects for employment and further study for our graduates. Former MSc in Statistics students have taken up positions in consulting firms, banks, and in the public sector where there is a shortage of well-qualified statisticians. Many go on to take higher degrees.
The normal entry requirement is an upper second class honours degree, or equivalent, with a significant mathematical content. Well-qualified applicants who do not meet this requirement will be considered on merit.
Our taught postgraduate courses are based around lectures, with problem classes and computer workshops. Most courses are assessed by a two-hour exam in the summer term although some contain an element of course work.
The courses in the programme are divided into two categories: compulsory courses and optional courses. Students must take courses to the value of four full units.
MSc in Statistics
i. One compulsory course:
ii. Optional courses to the value of three full units from the following:
Please note: Any other courses may be taken, except: ST433, ST436, ST437, ST438, ST439, ST440, any courses indexed FM, MA415, MA416 and MA420 (MA420 is not available in the 2012/13 session), with the proviso that the value of non-ST courses cannot exceed one full unit and they are taken with the permission of the Programme Director.
H = Half Unit; F = Full Unit.
MSc in Statistics (Research)
Applicants have the option to register for the MSc in Statistics (Research) branch of the programme in the first few weeks of the Michaelmas (Autumn) term. The research branch is similar to the MSc in Statistics nine-month programme but involves a compulsory dissertation (ST499) which replaces one unit’s worth of optional courses.
i. Two compulsory courses (both full unit):
ii. Optional courses to the value of two full units, as listed above for the MSc in Statistics.
ST425 Statistical Inference: Principles, Methods and Computation (full unit)
Core syllabus: A comprehensive coverage of fundamental aspects in probability and statistics. Data illustration using package R constitutes an integral part of the course, providing hands-on experience in simulation and data analysis.
Content: Random variables, Probability distributions, Probability inequality, Convergence of random variables, Point estimation, Hypothesis testing, Interval Estimation, Linear regression.
Course guide ST425
ST499 Dissertation (full unit) *for MSc in Statistics (Research) only
Core syllabus: Independent project work on a subject chosen by the student.
Content: Subjects are chosen and a supervisor assigned by week eight of the Michaelmas term. Students meet with their project supervisor and write an outline of the project before the end of Lent term. Students then spend July, August and September working on their projects.
Course guide ST499
ST405 Multivariate Methods (half unit)
Core syllabus: An introduction to the theory and application of modern multivariate methods used in the Social Sciences.
Content: A selection from the following topics: cluster analysis, multi-dimensional scaling, principal components analysis, correspondence analysis, factor analysis, latent variable models, multivariate normal distribution, exponential family, and structural equations models.
Course guide ST405
ST409 Stochastic Processes (half unit)
Core syllabus: A broad introduction to stochastic processes for postgraduates with an emphasis on financial and actuarial applications.
Content: Martingales, Markov Chains, Poisson Processes, Brownian motion, stochastic differential equations and diffusion processes. Applications in Finance. Actuarial applications.
Course guide ST409
ST411 Generalised Linear Modelling and Survival Analysis (half unit)
Core syllabus: Regression analysis and generalized linear modelling with an emphasis on diagnostics and the exponential family.
Content: One variable and multiple regression. Factorial design. Variable selection and model building. Deletion diagnostics. Transformation of the response, constructed variables. The use of R for data analysis. Exponential family and generalized linear models. Log-linear models, contingency tables, exact tests.
Course guide ST411
ST416 Multilevel and Longitudinal Modelling (half unit)
Core syllabus: A practical introduction to multilevel modelling with applications in social research.
Content: This course deals with the analysis of data from hierarchically structured populations (eg individuals nested within households or geographical areas) and longitudinal data. Multilevel (random-effects) extensions of standard statistical techniques, including multiple linear regression and logistic regression, will be considered. The course will have an applied emphasis with computer sessions using appropriate software (eg Stata).
Course guide ST416
ST418 Non-Linear Dynamics and the Analysis of Real Time Series (half unit)
Core syllabus: An introduction to the dynamics of non-linear deterministic systems with a practical focus, including case studies, of use of time series data in industry.
Content: Analysis and modelling of real data, involving an introduction to the dynamics of non-linear systems. Focus is on evaluating which methods to employ (linear/non-linear, deterministic/stochastic) in a given problem. Concrete applications in economics (electricity demand) and environment (weather derivatives) as well as analytically tractable illustrations.
Dynamics of nonlinear systems. Analysis and forecasting of nonlinear stochastic systems. Fractal dimensions and Lyapunov exponents. Concrete applications in forecasting electricity demand and pricing weather derivatives. Practical focus on the use of time series data in industry.
Course guide ST418
ST421 Developments in Statistical Methods (half unit)
Core syllabus: Our aim is to teach students important statistical methodologies that reflect the exciting development of the subject over the last ten years, which include empirical likelihood, MCMC, bootstrap, local likelihood and local fitting, model Assessment and selection methods, boosting, support vector machines. These are computationally intensive techniques that are particularly powerful in analysing large-scale data sets with complex structure.
Content: A selection from the following topics. Robustness of likelihood approaches: distance between working model and "truth", maximum likelihood under wrong models, quasi-MLE, model selection with AIC, robust estimation. Empirical likelihood: empirical likelihood of mean. Bayesian methods and Markov chain Monte Carlo (MCMC) basic Bayes, Gibbs sampler, Metropolis-Hastings algorithm. Elements of statistical learning: global fitting versus local fitting, linear methods for regression, splines, kernel methods and local likelihood. Model Assessment and selection: bias-variance trade-off, effective number of parameters, BIC, cross-validation. Further topics: additive models, varying-coefficient linear models, boosting, neural network, support vector machines. The course will be continuously updated to reflect important new developments in statistics.
Course guide ST421
ST422 Time Series (half unit)
Core syllabus: A broad introduction to statistical time series analysis for postgraduates
Content: What time series analysis can be useful for; autocorrelation; stationarity; basic time series models: AR, MA, ARMA; trend removal and seasonal adjustment; invertibility; spectral analysis; estimation; forecasting. If time permits, we will also discuss some of the following topics: financial time series and the (G)ARCH model; nonstationarity; bivariate time series.
Course guide ST422
ST435 Advanced Probability Theory (half unit)
Core syllabus: The course covers core topics in measure theoretic probability and modern stochastic calculus, thus laying a rigorous foundation for studies in statistics, actuarial science, financial mathematics, economics, and other areas where uncertainty is essential and needs to be described with advanced probability models. Emphasis is on probability theory as such rather than on special models occurring in its applications.
Content: Brief revision of mathematical tools: set theory, logics, techniques of proof, real and complex numbers, sequences, functions, metric spaces, notions of limits and convergence, continuity, differentiation and integration. Brief review of basic probability concepts in a measure theoretic setting: probability spaces, random variables, expected value, conditional probability and expectation, independence. Construction of probability spaces with emphasis on stochastic processes. Operator methods in probability: generating functions, moment generating functions, Laplace transforms, and characteristic functions.
Course guide ST435
EC484 Econometric Analysis (full unit)
Core syllabus: An advanced treatment of the theory of estimation and inference for econometric models.
Content: Part (a) Matrix background; symptotic statistical theory: modes of convergence, asymptotic unbiasedness, stochastic orders of magnitude, central limit theorems, applications to linear regression. Part (b) Non-linear-in variables systems: maximum likelihood and instrumental variables estimates, optimal instrumental variables estimates for static and dynamic models, and models with autocorrelated disturbances. Simultaneous equations systems, identification, estimation, asymptotic behaviour of estimators and hypothesis testing. Wald, generalised likelihood ratio and Lagrange multiplier hypothesis tests, asymptotic null and local behaviour and consistency.
Course guide EC484
MY456 Survey Methodology (half unit)
Core syllabus: This course provides an introduction to the methodology of the design and analysis of social surveys. It is intended both for students who plan to design and collect their own surveys, and for those who need to understand and use data from existing large-scale surveys.
Content: Topics covered include basic ideas of target populations, survey estimation and inference, sampling error and nonsampling error; sample design and sampling theory; methods of data collection; survey interviewing; cognitive processes in answering survey questions; design and evaluation of survey questions; nonresponse error and imputation for item nonresponse; survey weights; analysis of data from complex surveys; accessing, preparing and working with secondary data from existing social surveys. The course includes computer classes, using the statistical computer package Stata; no previous knowledge of Stata is required.
Course guide MY456
MY457 Causal Inference for Observational and Experimental Studies (half unit)
Core Syllabus: This course provides an introduction to statistical methods used for causal inference in the social sciences.
Content: Using the potential outcomes framework of causality, topics covered include research designs such as randomized experiments and observational studies. We explore the impact of noncompliance in randomized experiments, as well as nonignorable treatment assignment in observational studies. To analyze these research designs, the methods covered include matching, instrumental variables, difference-in-difference, and regression discontinuity. Examples are drawn from different social sciences. The course includes computer classes, where standard statistical computer packages (Stata or R) are used for computation.
Course guide MY457
MY459 Special topics in Quantitative Analysis (half unit)
Core syllabus: The course will assume knowledge of standard regression models, to the level covered in MI452. Please note that the exact topic changes every year. Content: The aim of the course is to introduce students to advanced analytic methods frequently used in leading-edge social research.
Course guide MY459
OR406 Mathematical Programming: Theory and Algorithms (half unit)
Core syllabus: To cover the use of mathematical programming models in practice, and an introduction to the theory and computational methods.
Content: As described under the headings of the lecture courses below.
OR406.1 Foundations of Mathematical Programming: An introduction to the mathematical foundations of mathematical programming
OR406.2 Mathematical Programming: Introduction to theory and the solution of linear and nonlinear programming problems: simplex and interior point algorithms, integer linear programming (ILP) methods (branch and bound, enumeration, cutting planes), decomposition methods, quadratic programming.
Course guide OR406
SA481 Basic Population Analysis (half unit)
Core syllabus: This course covers the basic principles and techniques of population analysis. Topics covered include the analysis of mortality, fertility, nuptiality, and migration, as well as the basic principles of population projection.
Content: The construction, interpretation, and uses of life tables. The measurement and analysis of fertility and birth intervals. Natural fertility and the proximate determinants of fertility, including Bongaarts' framework. Cohort and period approaches to measurement. Nuptiality and reproductivity. The basic measurement of migration. Component population projections. The use of models in demography.
Course guide SA481
For general information on the MSc programme please email MSc Enquiries at email@example.com.
For advice on your application please refer to the Frequently Asked Questions section of the Graduate Admissions website.
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