Accessibility statement

Further Statistics for Actuarial Science - MAN00065H

« Back to module search

  • Department: The York Management School
  • Credit value: 20 credits
  • Credit level: H
  • Academic year of delivery: 2023-24
    • See module specification for other years: 2024-25

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 Semester 1 2023-24

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 are part of the professional syllabus, including generalised linear models (GLMs), machine learning, extreme value theory, the modelling of dependent risks and mortality projection models.

Module learning outcomes

After successful completion the student is able to:

Subject content

  • explain the main theory and concepts of generalised linear models (GLMs);

  • apply GLMs to data and demonstrate knowledge of their importance in actuarial applications;

  • demonstrate knowledge of the main concepts of machine learning and explain their relevance to actuarial science;

  • demonstrate understanding of the main ideas of extreme value theory and apply these to actuarial loss modelling problems;

  • explain how dependence may be modelled at a deeper level than correlation and apply copulas to dependence modelling problems;

  • apply mortality projection models to data including the Lee-Carter model

Academic and graduate skills

  • present statistical analyses in a logical, rigorous, and concise way.

  • strict logical reasoning from assumptions to conclusion;

  • critically assess assumptions necessary to draw certain conclusions.

Module content

Syllabus:

  • Generalised linear models (GLMs)

  • Introduction to machine learning

  • Extreme value theory (EVT)

  • Copulas and dependence

  • Mortality projection

Indicative assessment

Task % of module mark
Closed/in-person Exam (Centrally scheduled) 70
Essay/coursework 30

Special assessment rules

None

Indicative reassessment

Task % of module mark
Closed/in-person Exam (Centrally scheduled) 70
Essay/coursework 30

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

McCullagh and Nelder (1989), Generalized Linear Models (2nd ed), Chapman and Hall/CRC

Friedman, Tibshirani, Hastie (2009), The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer Verlag

McNeil, A, Frey, R and Embrechts, P (2016), “Quantitative Risk Management: Concepts, Techniques & Tools” (2nd ed), Princeton University Press.


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.