Module leader: H Ogden
Please see the full Module Specifications for background information relating to all of the APTS modules, including how to interpret the information below.
Aim: To introduce important aspects of statistical modelling, including model selection, various extensions to generalised linear models, and non-linear models.
Learning outcomes: After taking this module, students should be able to:
- Provide a theoretical justification for the use of various criteria for model selection, and apply these techniques in practice.
- Describe some reasons why Generalised Linear Models may fail to fit real data well, and apply techniques to diagnose such failures.
- Describe some commonly-used extensions to Generalised Linear Models, and conduct frequentist and Bayesian inference for these models.
Prerequisites: Preparation for this module should (re-)establish familiarity with linear and generalized linear models, and with likelihood and Bayesian inference. Students who are familiar with (for example) chapters 4, 8, 10 and 11 of Davison (2003) ``Statistical Models'' will be very well prepared (and will already know something of the areas to be covered in the module).
- Principles and practice of model selection;
- Extensions of the Generalised Linear Model, including models for overdispersion and mixed-effects models;
- Non-linear models.
Assessment: Exercises set by the module leader, which will include some practical data analysis and statistical modelling.
- Davison (2003). Statistical Models.
- Gelman and Hill (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models.