- Department: Mathematics
- Credit value: 20 credits
- Credit level: H
- Academic year of delivery: 2024-25
- See module specification for other years: 2023-24
This module introduces various models and methods for multivariate data analysis as well as data-driven nonparametric models and methods.
Pre-requisite modules
Co-requisite modules
- None
Prohibited combinations
Occurrence | Teaching period |
---|---|
A | Semester 2 2024-25 |
This module introduces various models and methods for multivariate data analysis as well as data-driven nonparametric models and methods.
By the end of the module, students should be able to:
1. Work with different models for multivariate data and nonparametric models.
2. Apply the main techniques of multivariate data analysis and nonparametric estimation methods, and pick appropriate techniques to apply to different types of data.
3. Use the statistical package R to analyse multivariate data
This module can be split into two parts. Part (i) covers seven classic topics in multivariate data analysis to be taught in the first seven weeks; and part (ii) introduces some commonly-used nonparametric models and methods including the kernel and local polynomial estimations to be taught in the last three weeks.
Task | % of module mark |
---|---|
Closed/in-person Exam (Centrally scheduled) | 100 |
None
Task | % of module mark |
---|---|
Closed/in-person Exam (Centrally scheduled) | 100 |
Current Department policy on feedback is available in the student handbook. Coursework and examinations will be marked and returned in accordance with this policy.
T.W. Anderson. An Introduction to Multivariate Statistical Analysis. New York : Wiley, 2003.
C. Chatfield and A. J. Collins. Introduction to Multivariate Analysis. Chapman and Hall, 1980.
B. Everitt. An R and S-plus Companion to Multivariate Analysis. Springer, 2005.
J. Fan and I, Gijbels. Local Polynomial Modelling and Its Applications. Chapman and Hall/CRC, 1996.
K. V. Mardia, J. T. Kent and J. M. Bibby. Multivariate Analysis. Academic Press, 1979.