Aims: To present the theory and application of generalised linear models, including estimation, hypothesis testing and confidence intervals. To describe methods of model choice and model checking.
Learning Outcomes:
On completing the course, students should be able to
* choose an appropriate generalised linear model for a given set of data;
* fit this model using R, select terms for inclusion in the model and assess the adequacy of a selected model;
* make inferences on the basis of a fitted model and recognise the assumptions underlying these inferences and possible limitations to their accuracy.
Content:
Generalised linear models: Exponential families, standard form, linear predictors and link functions, deviance. Statement of asymptotic theory for the generalised linear model, Fisher information. Vector and matrix representation.
Model building: Subset selection and stepwise regression methods. Effects of collinearity in regression variables. Model checking including residuals AIC and BIC.
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