Students
ACST8084 – Survival Models
2026 – Session 1, In person-scheduled-weekday, North Ryde
General Information
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| Unit convenor and teaching staff |
Unit convenor and teaching staff
Yanlin Shi
|
|---|---|
| Credit points |
Credit points
10
|
| Prerequisites |
Prerequisites
STAT8310
|
| Corequisites |
Corequisites
|
| Co-badged status |
Co-badged status
|
| Unit description |
Unit description
This unit provides sophisticated statistical and probabilistic models for survival, sickness, insurance losses and other actuarial problems based on survival data. Techniques of survival analysis are used to estimate survival and loss distributions and evaluate risk factors in actuarial applications. Methods of both nonparametric and parametric estimation are utilised. Advanced models based on Markov chains and processes will be introduced to capture the features of stochastic transitions between different survival or loss states and to estimate the transition rates. Methods for valuing cashflows that are contingent upon multiple transition events and methods of projecting and valuing such expected cashflows will also be covered. Students gaining a weighted average of credit across all of ACST8084, ACST8085 and the CS2-related components of the assessment in ACST8086 (minimum mark of 60% on all three components) will satisfy the requirements for exemption from the professional subject CS2 of the Actuaries Institute. |
Important Academic Dates
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
Learning Outcomes
On successful completion of this unit, you will be able to:
- ULO1: Apply and analyse different types of survival models and justify their connections with practical actuarial problems.
- ULO2: Apply statistical inference technique to estimate parameters and probability distributions of survival models.
- ULO3: Demonstrate an understanding of the concepts and properties of Markov processes.
- ULO4: Solve Markov transition probabilities via matrix theory and differential equations.
- ULO5: Perform valuation of cashflows that are contingent upon multiple transition / decrement events.
- ULO6: Apply the various statistical techniques and quantitative methods in solving practical insurance problems.
General Assessment Information
Late Assessment Submission Penalty (written assessments)
If you submit your assessment late, 5% of the total possible marks will be deducted for each day (including weekends), up to 7 days. Submissions more than 7 days late will receive a mark of 0. Example 1 (out of 100): If you score 85/100 but submit 20 hours late, you will lose 5 marks and receive 80/100. Example 2 (out of 30): If you score 27/30 but submit 20 hours late, you will lose 1.5 marks and receive 25.5/30.
For any late submissions of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, students need to submit an application for Special Consideration.
Assessment Tasks
| Name | Weighting | Hurdle | Due | Groupwork/Individual | Short Extension | AI assisted? |
|---|---|---|---|---|---|---|
| Skills development: Quantitative analysis with R | 20% | No | 03/04/2026 | Individual | Yes | Open AI |
| Formal examination: Test | 20% | No | 28/04/2026 | Individual | No | Observed |
| Formal examination | 60% | No | Examination Period | Individual | No | Observed |
Skills development: Quantitative analysis with R
Assessment Type 1: Problem-based task
Indicative Time on Task 2: 20 hours
Due: 03/04/2026
Weighting: 20%
Groupwork/Individual: Individual
Short extension 3: Yes
AI assisted?: Open AI
The purpose of this assessment is for you to develop expertise in solving comlex statistical problems with the industry-standard software R.
You will work on solutions to a series of statistical problems, demonstrating your ability to analyse and interpret data effectively.
Skills in focus:
- Discipline knowledge
- Digital skills
- Critical thinking and problem solving
Deliverable(s): Written submission
Individual assessment
On successful completion you will be able to:
- Apply and analyse different types of survival models and justify their connections with practical actuarial problems.
- Apply statistical inference technique to estimate parameters and probability distributions of survival models.
- Solve Markov transition probabilities via matrix theory and differential equations.
- Perform valuation of cashflows that are contingent upon multiple transition / decrement events.
- Apply the various statistical techniques and quantitative methods in solving practical insurance problems.
Formal examination: Test
Assessment Type 1: Examination
Indicative Time on Task 2: 17 hours
Due: 28/04/2026
Weighting: 20%
Groupwork/Individual: Individual
Short extension 3: No
AI assisted?: Observed
The purpose of this assessment is for you to demonstrate your understanding and knowledge of key topics from the unit.
You will participate in a formal test. Feedback on your performance will help you assess your progress through the unit content.
Deliverable(s): Test
Individual assessment
On successful completion you will be able to:
- Apply and analyse different types of survival models and justify their connections with practical actuarial problems.
- Apply statistical inference technique to estimate parameters and probability distributions of survival models.
- Demonstrate an understanding of the concepts and properties of Markov processes.
- Apply the various statistical techniques and quantitative methods in solving practical insurance problems.
Formal examination
Assessment Type 1: Examination
Indicative Time on Task 2: 28 hours
Due: Examination Period
Weighting: 60%
Groupwork/Individual: Individual
Short extension 3: No
AI assisted?: Observed
The purpose of this assessment is for you to formally demonstrate the expertise you have gained in this unit.
You will participate in a 3-hour exam held during the University Examination period. Important information about the exam will be made available on the unit iLearn page. You should also review the MQ Exams website for general tips.
Deliverable(s): Formal exam
Individual assessment
On successful completion you will be able to:
- Apply and analyse different types of survival models and justify their connections with practical actuarial problems.
- Apply statistical inference technique to estimate parameters and probability distributions of survival models.
- Demonstrate an understanding of the concepts and properties of Markov processes.
- Solve Markov transition probabilities via matrix theory and differential equations.
- Perform valuation of cashflows that are contingent upon multiple transition / decrement events.
- Apply the various statistical techniques and quantitative methods in solving practical insurance problems.
1 If you need help with your assignment, please contact:
- the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
- the Writing Centre for academic skills support.
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation.
3 An automatic short extension is available for some assessments. Apply through the Service Connect Portal.
Delivery and Resources
Please refer to iLearn for details.
Unit Schedule
Week 1: Probability models (revision); Survival analysis
Week 2: Estimation of survival distributions
Week 3: Variance estimation and confidence intervals
Week 4: Cox proportional hazards models
Week 5: Cox proportional hazards models; Stochastic processes;
Week 6: Markov chains; Due of Individual Assignment
Week 7: Markov chains;
Week 8: Markov jump processes; Class test
Week 9: Markov jump processes
Week 10: Applications of Markov processes
Week 11: Applications of Markov processes
Week 12: Competitive risks and multiple decrement tables
Week 13: Revision
Note: This is only a tentative schedule. The actual schedule will be adjusted from time to time in accordance with the progress of lectures.
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- Special Consideration Policy
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Unit information based on version 2026.02 of the Handbook