Description
The focus of this subject is stochastic and survival modelling. Stochastic processes are typically used to model the dynamic behaviour of random variables indexed by time. The close-of-day exchange rate is an example of a discrete-time stochastic process. There are also continuous-time stochastic processes that involve continuously observing variables, such as the water level within significant rivers. This subject covers simple discrete Markov chains, continuous-time stochastic processes and some simple time-series models. Further, the theory, estimation and application of a variety of survival models are covered, spanning parametric, semi-parametric and non-parametric models.
Subject details
| Type | Undergraduate |
| Code | ACSC13-304 |
| EFTSL | 0.125 |
| Faculty | Bond Business School |
| Credit | 10 |
| Study areas |
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| Subject fees |
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Learning outcomes
1. Determine the type of a stochastic process and whether it possesses certain well-known properties.
2. Define, estimate and analyse Markov chains, including their long-run behaviour.
3. Define, estimate and analyse Markov jump processes.
4. Demonstrate an understanding of censoring and lifetime random variables in actuarial modelling.
5. Estimate and analyse a variety survival models, including Weibull, Gompertz, Kaplan-Meier, Nelson-Aalen, Cox Proportional Hazards, Markov multi-state, Binomial and Poisson models.
6. Estimate and analyse some basic time-series models, including ARIMA modelling.
7. Demonstrate an understanding of the benefits of machine learning techniques in actuarial applications.
8. Use a statistical package frequently used by practitioners to model stochastic processes and survival models.
Enrolment requirements
| Requisites: ? | Nil |
|---|---|
| Assumed knowledge: | Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge. Assumed Prior Learning (or equivalent):Possess demonstratable knowledge in mathematical statistics and probability theory to the level of a unit such as ACSC12-200 Mathematical Statistics. |
| Restrictions: ? | Nil |
Subject outlines
Subject dates
Future offerings not yet planned.