This module will introduce and develop flexible statistical modelling methods that allow for general and complex forms of data to be modelled, extending ideas already encountered in earlier modules on linear and/or generalised linear modelling. The two main foci of the syllabus will be methods for modelling grouped data using random effects, and non-parametric “smoothing” methods for modelling data with complex functional form.
pre-requisite: (MATH2010 and MATH3091) or STAT6083
Aims and Objectives
Having successfully completed this module you will be able to:
- Understand and apply basic criteria that define successful statistical modelling.
- Understand the theoretical underpinnings of the discussed modelling methods, and their implementation in appropriate statistical software.
- Understand the concept of random effects, and their role in modelling common forms of grouped data.
- Apply common nonparametric modelling methods to data with both single and multiple predictor variables.
- Fundamentals of random effect modelling and population versus subject-specific models.
- Linear mixed models for continuous data, generalised least squares and restricted maximum likelihood.
- Generalised linear mixed models for discrete data, likelihood estimation and numerical methods.
- Common univariate smoothing methods including kernel smoothing and smoothing splines.
- Generalised additive models.
- Cross-validation and bias-variance trade-off for selecting smoothing parameters.
Learning and Teaching
Teaching and learning methods
36 Lectures and/or problem classes.
|Total study time||150|
Resources & Reading list
Wood, S. N. (2017). Generalized Additive Models: An Introduction with R. CRC Press.
Faraway, J. J. (2016). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models.. CRC Press.
Demindenko, E. (2013). Mixed Models: Theory and Applications with R. Wiley..
The assessment for the repeat candidates will be based completely on the final examination.
This is how we’ll formally assess what you have learned in this module.
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Repeat type: Internal & External