Further Statistics for Actuarial Science - MAN00049H
- Department: The York Management School
- Credit value: 10 credits
- Credit level: H
- Academic year of delivery: 2022-23
Module summary
The aim of the module is to expose students to a number of advanced statistical topics that are used in actuarial science and quantitative risk management, including Bayesian inferential procedures, credibility theory, extreme value theory, the modelling of dependent risks and machine learning
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Autumn Term 2022-23 |
Module aims
The aim of the module is to expose students to a number of advanced statistical topics that are used in actuarial science and quantitative risk management, including Bayesian inferential procedures, credibility theory, extreme value theory, the modelling of dependent risks and machine learning
Module learning outcomes
After successful completion the student is able to:
Subject content
- apply Bayes and minimax decision rules;
- use Bayesian inferential procedures;
- apply Bayesian credibility theory to insurance tarification problems;
- describe the main ideas of extreme value theory and dependence modelling via copulas;
- demonstrate knowledge of the main concepts of machine learning.
Academic and graduate skills
- present decision theoretic analyses in a logical, rigorous, and concise way.
- strict logical reasoning from assumptions to conclusion;
- critically assess assumptions necessary to draw certain conclusions.
Indicative assessment
| Task | % of module mark |
|---|---|
| Closed/in-person Exam (Centrally scheduled) | 80 |
| Essay/coursework | 20 |
Special assessment rules
None
Indicative reassessment
| Task | % of module mark |
|---|---|
| Closed/in-person Exam (Centrally scheduled) | 100 |
Module feedback
Students will receive feedback within three weeks of the hand-in problem sets. The feedback will be handed to students personally and takes the form of comments and suggestions for improvement written on the handed in work.
Indicative reading
McNeil, A, Frey, R and Embrechts, P (2016), “Quantitative Risk Management: Concepts, Techniques & Tools” (2nd ed), Princeton University Press.