| Unit name | Generalised Linear Models 34 |
|---|---|
| Unit code | MATHM5200 |
| Credit points | 10 |
| Level of study | M/7 |
| Teaching block(s) |
Academic Year (weeks 1 - 52) |
| Unit director | Dr. Liverani |
| Open unit status | Not open |
| Pre-requisites |
None |
| Co-requisites |
None |
| School/department | School of Mathematics |
| Faculty | Faculty of Science |
We study methods for the analysis of data in which one variable, the response, is influenced systematically by one or more explanatory variables, which could be qualitative or quantitative in nature, in addition to the presence of random variation. In contrast to traditional methods involving linear models and normal variation, here we depart from linearity and normality. Instead of relying on least squares we employ the principle of maximum likelihood, but also investigate alternatives based on the idea of sparsity.
The topics discussed will be: &� Generalized linear models: extensions of the ideas of linear modelling to deal with situations where the response variable takes integer or categorical values. These methods are particularly important in biomedical applications. We also look into model validation and develop robust strategies to detect departures from the model like outliers. &� Survival analysis: an introduction to regression models for lifetime data, used in clinical trials and industrial testing.
