This module is not currently running in 2024 to 2025.
Statistics methods contribute significantly to areas such as biology, ecology, sociology and economics. The real data collected does not always follow standard statistical models. This module looks at modern statistical models and methods that can be utilised for such data, making use of R programs to execute these methods.
Indicative module content: Motivating examples; model fitting through maximum likelihood for specific examples; function optimization methods; profile likelihood; score tests; Wald tests; confidence interval construction; latent variable models; EM algorithm; mixture models; simulation methods; importance sampling; kernel density estimation; Monte Carlo inference; bootstrap; permutation tests; R programs.
In addition, for level 7 students: advanced EM algorithm methods, advanced simulation methods, writing R programs for advanced methods and applications.
Total contact hours: 38
Private study hours: 112
Total study hours: 150
80% Examination, 20% Coursework
Morgan, B. J. T. (2009) Applied Stochastic Modelling, Chapman and Hall.
See the library reading list for this module (Canterbury)
The intended subject specific learning outcomes.
On successfully completing the level 7 module students will be able to:
1. demonstrate systematic understanding of computational statistics;
2. demonstrate the capability to solve complex problems using a very good level of skill in calculation and manipulation of the material in the following areas: Numerical
aspects of maximum likelihood estimation, EM algorithm and simulation methods, including advanced techniques;
3. apply a range of concepts and principles in computational statistics in loosely defined contexts, showing good judgment in the selection and application of tools and
techniques;
4. write R programs for complex applications, making effective and well-considered use of R.
The intended generic learning outcomes.
On successfully completing the level 7 module students will be able to:
1. work competently and independently, be aware of their own strengths and understand when help is needed;
2. demonstrate a high level of capability in developing and evaluating logical arguments;
3. communicate arguments confidently with the effective and accurate conveyance of conclusions;
4. manage their time and use their organisational skills to plan and implement efficient and effective modes of working;
5. solve problems relating to qualitative and quantitative information;
6. make effective use of information technology skills such as online resources (Moodle), internet communication;
7. communicate technical material effectively;
8. demonstrate an increased level of skill in numeracy and computation;
9. demonstrate the acquisition of the study skills needed for continuing professional development.
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