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 also introduces simple discrete Markov chains and continuous-time stochastic processes.
|Faculty||Bond Business School|
|Study abroad||Available to Study Abroad students|
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, both time-homogeneous and time-inhomogeneous.
4. Demonstrate an understanding of censoring and lifetime random variables in survival modelling, including the ability to perform calculations involving lifetime random variables.
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. Describe and perform multiple hypothesis tests applicable to survival modelling.
7. Demonstrate an understanding of the benefit of smoothing/graduation and the key trade-off involved.
8. Use statistical software commonly used by practitioners to model stochastic processes and survival models.
Please check the subject outline for pre-requisite subjects.