Survival Analysis - MA525

Location Term Level Credits (ECTS) Current Convenor 2019-20
(version 2)
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6 15 (7.5) MR P McQuire


MACT5160 (Actuarial mathematics 1); MAST5007 Mathematical statistics





The aim of this module is to provide a grounding in mathematical and statistical modelling techniques that are of particular relevance to survival analysis and their application to actuarial work.

Calculations in life assurance, pensions and health insurance require reliable estimates of transition intensities/survival rates. This module covers the estimation of these intensities and the graduation of these estimates so they can be used reliably by insurance companies and pension schemes. The syllabus also includes the study of various other survival models, and an introduction to machine learning. This module will cover a number of syllabus items set out in Subject CS2 – Actuarial Mathematics published by the Institute and Faculty of Actuaries.


This module appears in:

Contact hours

42 hours

Method of assessment

70% Examination, 30% Coursework

Indicative reading

Study notes published by the Actuarial Education Company for Subject CS2.
Modelling Mortality with Actuarial Applications, MacDonald, Richards, Currie (2018)

See the library reading list for this module (Canterbury)

Learning outcomes

The intended subject specific learning outcomes. On successfully completing the level 6 module students will be able to:

1 describe, interpret and discuss key aspects of survival models;
2 demonstrate the capability to deploy established approaches accurately to analyse and solve problems using a reasonable level of skill in calculation and manipulation of survival models;
3 demonstrate an appreciation of recent developments in survival models and the links between the theory of survival models and their practical application in well-defined contexts.

The intended generic learning outcomes. On successfully completing the level 6 or 7 module students will be able to:

1 develop a logical mathematical approach to solving complex problems including cases where information/data is not complete
2 demonstrate skills in written communication to both technical and non-technical audiences,
3 demonstrate skills in the use of relevant information technology,
4 demonstrate skills in time management, organisation and studying so that tasks can be planned and implemented at a professional level.

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