A minimum of 2.2, with a substantial amount of mathematics at university level.
All applicants are considered on an individual basis and additional qualifications, professional qualifications and relevant experience may also be taken into account when considering applications.
Please see our International website for entry requirements by country and other relevant information. Due to visa restrictions, international fee-paying students cannot study part-time unless undertaking a distance or blended-learning programme with no on-campus provision.
The University requires all non-native speakers of English to reach a minimum standard of proficiency in written and spoken English before beginning a postgraduate degree. Certain subjects require a higher level.
For detailed information see our English language requirements web pages.
Please note that if you are required to meet an English language condition, we offer a number of pre-sessional courses in English for Academic Purposes through Kent International Pathways.
Duration: 1 year full-time
You undertake a substantial project in statistics, supervised by an experienced researcher. Some projects are focused on the analysis of particular complex data sets while others are more concerned with generic methodology.
You gain experience of analysing real data problems through practical classes and exercises. The programme includes training in the computer language R.
The following modules are indicative of those offered on this programme. This list is based on the current curriculum and may change year to year in response to new curriculum developments and innovation. Most programmes will require you to study a combination of compulsory and optional modules. You may also have the option to take modules from other programmes so that you may customise your programme and explore other subject areas that interest you.
This applied statistics module focusses on problems that occur in the fields of ecology, biology, genetics and psychology. Motivated by real examples, you will learn how to define and fit stochastic models to the data. In more complex situations this will mean using optimisation routines in MATLAB to obtain maximum likelihood estimates for the parameters. You will also learn how construct, fit and evaluate such stochastic models. Outline Syllabus includes: Function optimisation. Basic likelihood tools. Fundamental features of modelling. Model selection. The EM algorithm. Simulation techniques. Generalised linear models.
The module enables students to undertake an independent piece of work in a particular area of statistics, or statistical finance/financial econometrics and to write a coherent account of the material.
There is no specific syllabus for this module.
A list of possible topics, together with names of Staff willing to supervise these projects, will be circulated to students in the autumn term. A broad range of projectsis available, encompassing both practical data analysis and more methodological work, although projects that are primarily theoretical will typically have obvious practical applications. Students then choose a topic after consultation and agreement with the relevant member of staff. This is done early in the spring term and some preliminary work is done during the spring term, leading to a short presentation at the end of that term. The main part of the project is then undertaken after the examinations in May.
This course introduces (and revises for some students) the essentials of probability and classical (frequentist) statistical inference, which provide the backbone for later modules.
Syllabus: Probability: axioms, marginal, joint and conditional distributions, Bayes theorem, important distributions, generating functions and various modes of convergence. Classical Inference: Sampling
distributions. Point estimation: consistency, Cramer-Rao inequality, efficiency, sufficiency, minimum variance unbiased estimators. Likelihood. Methods of estimation. Hypothesis tests: maximum likelihood-ratio test, Wald and score tests, profile and test-based confidence intervals.
Linear model. Least squares. General linear model; simple and multiple regression, polynomial regression. Model selection, residuals, outliers, diagnostics. Analysis of variance. Generalised linear model.
Discrete data analysis. Review of Binomial, Poisson, negative binomial and multinomial distributions. Properties, estimation, hypothesis tests.
Contingency tables. Tests for independence. Measures of association. Logistic models.
Multidimensional tables. Log–linear models; fitting and model selection.
Bayes Theorem for density functions; Conjugate models; Predictive distribution; Bayes estimates; Sampling density functions; Gibbs and Metropolis-Hastings samplers; Winbugs/OpenBUGS; Bayesian hierarchical models; Bayesian model choice; Objective priors; Exchangeability; Choice of priors; Applications of hierarchical models.
Sampling: Simple random sampling. Sampling for proportions and percentages. Estimation of sample size. Stratified sampling. Systematic sampling. Ratio and regression estimates. Cluster sampling. Multi-stage sampling and design effect. Questionnaire design. Response bias and non-response.
General principles of experimental design: blocking, randomization, replication. One-way ANOVA. Two-way ANOVA. Orthogonal and non-orthogonal designs. Factorial designs: confounding, fractional replication. Analysis of covariance.
Design of clinical trials: blinding, placebos, eligibility, ethics, data monitoring and interim analysis. Good clinical practice, the statistical analysis plan, the protocol. Equivalence and noninferiority. Sample size. Phase I, II, III and IV trials. Parallel group trials. Multicentre trials.
In addition, level 7 students will study hierarchical designs: fixed and random effects models; split-plot designs; crossover trials; variance components.
Introduction: Machine learning and data visualisation with R.
Classification and prediction: Generalised linear model (GLM), linear discrimination analysis (LDA), k-nearest neighbors (KNN). R-based worked examples.
Resampling methods: Cross-validation (CV) and bootstrap. R-based worked examples.
Regression tree-based methods: Classification and regression trees (CART), bagging, random forests and boosting. R-based worked examples.
Support vector machines (SVM): Support vector classifier, regression SVM. R-based worked examples.
Machine Learning in Action:
(a) Biomedical and health data analysis;
(b) Bond default data analysis;
(c) Insurance data analysis;
(d) Financial data analysis;
(e) Other big data analysis.
Each year three topics will be offered and will reflect recent advances in statistical modelling and statistical methodology. Example topics are:
a) Statistical Ecology: Understanding demographic parameters and how they are used to model population dynamics. Estimating abundance and the effect of heterogeneity. Models for estimating survival probabilities. Multi-site and multi-state models. Classical model-selection. Complex models. Case studies.
b) Survival analysis: Survival data, types of censoring. Failure times and hazard functions; Accelerated failure time model. Parametric models, exponential, piecewise exponential, Weibull. Nonparametric estimates: the Kaplan-Meier estimator, and asymptotic confidence regions. Parametric inference. Survival data with covariates. Proportional hazards. Cox's model and inference. Computer software: R and WinBUGS.
c) Regression models with many variables: Examples of high-dimensional problems; Penalized maximum likelihood; Ridge regression; non-negative garrote; Lasso and adaptive Lasso estimation; LARS algorithm; Oracle property; Elastic Net; Group lasso.
d) Modern nonparametric statistics: Bias-variance trade-off, Kernel density estimation, Kernel smoothing, Locally linear and locally quadratic estimation, basis function methods.
In addition, level 7 students will study advanced applications of these techniques (often using R) in all topics.
Multivariate normal distribution, Inference from multivariate normal samples, principal component analysis, mixture models, factor analysis, clustering methods, discrimination and classification, graphical models, the use of appropriate software.
This module will focus on basic features of stochastic processes and time series analysis. It includes: Markov chains on discrete state spaces, communication classes, transience and recurrence, positive recurrence, stationary distributions. Markov processes on discrete state spaces, exponential distribution, embedded Markov chain, transition graphs, infinitesimal generator, transition probabilities, stationary distributions, skip-free Markov processes. Stationary time series: Stationarity, autocovariance and autocorrelation functions, partial autocorrelation functions, ARMA processes. ARIMA Model Building and Testing: Estimation, Box Jenkins, criteria for choosing between models, diagnostic tests for the residuals of a time series after estimation. Forecasting: Holt-Winters, Box-Jenkins, prediction bounds.
Assessment is through coursework involving: complex theoretical questions, analysis of real-world data using appropriate computing packages over a range of areas of application; written unseen examinations; dissertation.
This programme aims to:
You will gain knowledge and understanding of:
You develop intellectual skills in:
You gain subject-specific skills in:
You will gain the following transferable skills:
The 2020/21 annual tuition fees for this programme are:
For details of when and how to pay fees and charges, please see our Student Finance Guide.
For students continuing on this programme fees will increase year on year by no more than RPI + 3% in each academic year of study except where regulated.* If you are uncertain about your fee status please contact firstname.lastname@example.org
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In The Complete University Guide 2020, the University of Kent was ranked in the top 10 for research intensity. This is a measure of the proportion of staff involved in high-quality research in the university.
Please see the University League Tables 2020 for more information.
In the Research Excellence Framework (REF) 2014, research by the School of Mathematics, Statistics and Actuarial Science was ranked 25th in the UK for research power and 100% or our research was judged to be of international quality.
An impressive 92% of our research-active staff submitted to the REF and the School’s environment was judged to be conducive to supporting the development of world-leading research.
Specific interests are in biometry, cluster analysis, stochastic population processes, analysis of discrete data, analysis of quantal assay data, overdispersion, and we enjoy good links within the University, including the School of Biosciences and the Durrell Institute of Conservation and Ecology. A recent major joint research project involves modelling the behaviour of yeast prions and builds upon previous work in this area. We also work in collaboration with many external institutions.
Current work includes non-parametric Bayes, inference robustness, modelling with non-normal distributions, model uncertainty, variable selection and functional data analysis.
Research covers bioinformatics (eg DNA microarray data), involving collaboration with the School of Biosciences. Other interests include population genetics, clinical trials and survival analysis.
Research focuses on empirical likelihood, high-dimensional data analysis, nonlinear dynamic analysis, semi-parametric modelling, survival analysis, risk insurance, functional data analysis, spatial data analysis, longitudinal data analysis, feature selection and wavelets.
Full details of staff research interests can be found on the School's website.
Branching processes in biology; cell division models; ecological statistics; generalised linear mixed models; identifiability.; parameter redundancy.View Profile
Shape analysis; directional statistics; image analysis.View Profile
Medical statistics and applied statistics.View Profile
Bayesian nonparametrics; MCMC, Urn models; Markov and Levy processes; Move-to-Front and Move-to-Root allocation rules.View Profile
Stochastic epidemic models; applied probability; simulation; statistical inference; goodness of fit; branching processes; martingales; medical education.View Profile
Integrated population modelling of dependent data structures.View Profile
Biometry; cluster analysis; stochastic population processes; psychological applications of statistics; multivariate analysis; simulation; analysis of quantal assay data; medical statistics; ecological statistics; overdispersion; estimation using transforms.View Profile
Bayesian nonparametric methods; copula function with its applications in finance; wavelet estimation methods.View Profile
Semi and non-parametric statistical modelling; statistical genetics with medical applications; Bayesian modelling; mixture models; neuroimaging.View Profile
Students often go into careers as professional statisticians in industry, government, research and teaching but our programmes also prepare you for careers in other fields requiring a strong statistical background. You have the opportunity to attend careers talks from professional statisticians working in industry and to attend networking meetings with employers.
Our graduates have started careers in diverse areas such as the pharmaceutical industry, financial services and sports betting.
The taught programmes in Statistics and Statistics with Finance provide exemption from the professional examinations of the Royal Statistical Society and qualification for Graduate Statistician status.
Kent’s Computing Service central facility runs Windows. Within the School, postgraduate students can use a range of UNIX servers and workstations. Packages available include R, SAS, MATLAB, SPSS and MINITAB.
Staff publish regularly and widely in journals, conference proceedings and books. Among others, they have recently contributed to: Annals of Statistics; Biometrics; Biometrika; Journal of Royal Society, Series B; Statistics and Computing. Details of recently published books can be found within our staff research interests.
All students registered for a taught Master's programme are eligible to apply for a place on our Global Skills Award Programme. The programme is designed to broaden your understanding of global issues and current affairs as well as to develop personal skills which will enhance your employability.
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