The focus of this subject is analysing the time until an event happens, such as the illness or death of a person, or the failure of a business. The issue of censored data is common in such scenarios and how to handle censored data will be discussed throughout this course. The theory, estimation and application of a variety of survival models for censored data are covered, spanning parametric, semi-parametric and non-parametric models. Machine learning methods suitable for censored data are also covered.
|Faculty||Bond Business School|
1. Demonstrate an advanced understanding of censoring and lifetime random variables.
2. Estimate, analyse and compare a variety of survival models, including parametric, non-parametric and proportional hazard models.
3. Critically evaluate the benefits of machine learning techniques in survival analysis.
4. Estimate and analyse machine learning models in the presence of censored data.
5. Use a statistical package frequently used by practitioners for survival analysis.
There are no co-requisites.
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.
Possess demonstratable knowledge of mathematical statistics and the mathematics of finance to the level of a unit such as ACSC71-200 Mathematical Statistics.
Future offerings not yet planned.