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ACSC71-302: Advanced Modelling

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 also introduces simple discrete Markov chains and continuous-time stochastic processes.

Subject details

TypePostgraduate
CodeACSC71-302
EFTSL0.125
FacultyBond Business School
Credit10
Study areas
  • Actuarial Science
Subject fees
  • Commencing in 2019: $4,890
  • Commencing in 2020: $5,070

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, 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.

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 ACSC71-200 Mathematical Statistics.

Restrictions: ?

Nil

Subject outlines

Subject dates

Standard Offering
Enrolment opens11/11/2018
Semester start14/01/2019
Subject start14/01/2019
Cancellation 1?28/01/2019
Cancellation 2?04/02/2019
Last enrolment27/01/2019
Withdraw – Financial?09/02/2019
Withdraw – Academic?02/03/2019
Teaching census?08/02/2019