Module manager: tbc
Email: tbc
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2026/27
None
| MATH3701 | Statistical Modelling |
This module is not approved as an Elective
This module develops advanced techniques for analysis of datasets. These include methods for estimating the probability density function from a data set, and approaches to constraining the effective number of variables that contribute in the fitting of a linear model, which are essential in many modern high-dimensional data sets.
The objective of the module is to equip students with the theoretical understanding and practical skills to apply smoothing, dimension reduction, and regularisation methods for building accurate and interpretable statistical models in complex data settings, including high-dimensional data.
Subject specific learning outcomes:
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
Derive and employ various smoothing techniques used in density estimation and nonparametric regression;
Explain the difficulties caused in regression analyses by multicollinearity and high-dimensional data;
Use dimension-reduction and penalisation techniques to conduct regression analyses on high-dimensional data;
Demonstrate proficiency in implementing and tuning advanced regression and smoothing methods, including cross-validation and hyperparameter optimisation;
Explain the theoretical foundations of bias-variance trade-off, regularisation penalties, and dimension reduction techniques; and
Use a statistical package with real data to fit these models to data and to write a report giving and interpreting the results.
Skills learning outcomes:
On successful completion of the module students will have demonstrated the following skills learning outcomes:
Demonstrate the ability to critically evaluate and apply advance statistical techniques (smoothing, regularisation, dimension reduction) to real-world data problems.
Interpret, and communicate complex statistical results.
Use of R statistical software to implement non-parametric and regularisation methods
Apply problem solving and analytical thinking to select appropriate modelling strategies under constraints such as limited data or high-dimensionality.
Kernel density estimation and regression smoothing, k-NN smoothing. Bandwidth selection by cross-validation.
Regression on high-dimensional data, such as principal component regression or partial least squares regression;
Penalised regression models: ridge regression, lasso regression, and elastic-net models, and their parameter estimation methods.
Additional topics that build on these may be covered as time allows. Further details of possible topics will be delivered closer to the time that the module runs.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 33 | 1 | 33 |
| Practical | 1 | 2 | 2 |
| Private study hours | 115 | ||
| Total Contact hours | 35 | ||
| Total hours (100hr per 10 credits) | 150 | ||
115
Formative feedback will be provided on regular example sets or other similar learning activity.
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
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