Postgraduate Course: Likelihood and Generalized Linear Models (MATH11121)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Not available to visiting students |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 10 |
| Home subject area | Mathematics |
Other subject area | None |
| Course website |
None |
Taught in Gaelic? | No |
| Course description | The concept of likelihood is central to statistical inference. This course develops likelihood methods for single parameter and multiparameter problems, including iterative maximum likelihood estimation, likelihood-based confidence regions and likelihood ratio tests.
An important application of likelihood methods is to generalized linear models which provide a broad framework for statistical modelling of discrete or continuous data. Special cases of such models are considered. |
Course Delivery Information
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| Delivery period: 2012/13 Semester 2, Not available to visiting students (SS1)
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WebCT enabled: Yes |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| No Classes have been defined for this Course |
| First Class |
First class information not currently available |
| Exam Information |
| Exam Diet |
Paper Name |
Hours:Minutes |
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| Main Exam Diet S2 (April/May) | MSc Likelihood and Generalized Models | 2:00 | | |
Summary of Intended Learning Outcomes
1. Familiarity with a wide range of statistical modelling situations.
2. Ability to apply likelihood based inference to real modelling situations.
3. Ability to apply likelihood methods to derive estimates, confidence regions and hypothesis tests.
4. Familiarity with examples of generalized linear models.
5. Ability to use R for statistical modelling and data analysis.
6. Ability to analyse data and interpret results of statistical analyses. |
Assessment Information
| Examination 100% |
Special Arrangements
| Undergraduates may take this course from 2012-13; students *must* have passed : Foundations of Calculus (MATH08005) AND Several Variable Calculus (MATH08006) AND Linear Algebra (MATH08007) AND Methods of Applied Mathematics (MATH08035) AND Probability (Year 2) (MATH08008) AND Statistics (Year 2) (MATH08051) |
Additional Information
| Academic description |
Not entered |
| Syllabus |
This syllabus is for guidance purposes only :
1. Statistical modelling and applications of likelihood.
2. Likelihood principles.
3. Likelihood based inference for single parameter and multiparameter models.
4. Wald, score, and likelihood ratio tests and related confidence regions.
5. Generalized likelihood ratio tests.
6. Applied aspects of maximum likelihood estimation, including graphical approaches, iterative estimation and the method of scoring.
7. Statistical modelling using generalized linear models, link functions and exponential family distributions.
8. Several examples of generalized linear models, including normal, Poisson, exponential, gamma and binary regression.
9. Diagnostic and residual analyses for generalized linear models.
10. Using R to fit and assess models. |
| Transferable skills |
Not entered |
| Reading list |
1. Azzalini, A., (1996). Statistical Inference Based on the Likelihood. Chapman & Hall, London.
2. Pawitan, Y. (2001). In All Likelihood: Statistical Modelling and Inference using Likelihood. Oxford University Press, Oxford.
3. Dobson, A.J. (2002). An Introduction to Generalized Linear Models, 2nd Edition. Chapman & Hall/CRC, London.
4. McCullagh, P. & Nelder, J.A. (1989). Generalized Linear Models, 2nd Edition. Chapman & Hall, London. |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | LGLM |
Contacts
| Course organiser | Dr Bruce Worton
Tel: (0131 6)50 4884
Email: |
Course secretary | Mrs Frances Reid
Tel: (0131 6)50 4883
Email: |
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