Program Overview

ACST8084 – Survival Models

General Information

The unit convenor and teaching staff for ACST8084 is Yanlin Shi. This unit is worth 10 credit points and has a prerequisite of STAT8310.

• 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 utilized. 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.

Learning Outcomes

On successful completion of this unit, students will be able to:

• Apply and analyse different types of survival models and justify their connections with practical actuarial problems.
• Apply statistical inference techniques 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.

General Assessment Information

Late Assessment Submission Penalty: Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of '0' will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11:55 pm. A 1-hour grace period is provided to students who experience a technical concern.

Assessment Tasks

• Assignment: 20% weighting, due on 11/04/2025
• Class Test: 20% weighting, due on 28/04/2025
• Final Exam: 60% weighting, due during the Examination Period

Assignment

• Assessment Type: Quantitative analysis task
• Indicative Time on Task: 20 hours
• Due: 11/04/2025
• Weighting: 20%
• Problem-solving questions requiring detailed solutions using the statistical package R.

Class Test

• Assessment Type: Quiz/Test
• Indicative Time on Task: 17 hours
• Due: 28/04/2025
• Weighting: 20%
• The test will be approximately 120 minutes, to be held during class time.

Final Exam

• Assessment Type: Examination
• Indicative Time on Task: 28 hours
• Due: Examination Period
• Weighting: 60%
• The final examination will be closed book, a three-hour written paper with ten minutes reading time, to be held during the University Examination period.

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
• Week 7: Markov chains; Due date for Individual Assignment
• 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

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