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Course Module Information
Course Modules
ST4040: Modern Statistical Methods
Semester 2 | Credits: 5
An overview of modern approaches to statistical practice. Topics include: mixture models, nonparametric regression and smoothing, linear mixed models, penalised regression, classification and regression trees, prediction modelling, dealing with missing data, survival analysis
(Language of instruction: English)
Learning Outcomes
- Recognise how mixtures arise in an applied setting and construct a basic Gaussian mixture model
- Describe nonlinear relationships between variables using spline and kernel smoothers
- Extend linear regression to the case of clustered data, explain the concept of a random effect and its distinction from a fixed effect, calculate an intra-class correlation coefficient
- Apply penalised regression methods such as the LASSO for variable selection
- Construct a classification/regression tree; apply trees for prediction
- Calculate and interpret sensitivity, specificity, positive predictive value, negative predictive value; draw and interpret a ROC curve
- Derive and apply suitable methods for missing data
- Apply Cox proportional hazards models for time to event data
Assessments
- Department-based Assessment (100%)
Teachers
- NOELLE GANNON:
Research Profile |
Email
- COLLETTE MCLOUGHLIN:
Research Profile |
Email
- ANDREW SIMPKIN:
Research Profile |
Email
Note: Module offerings and details may be subject to change.