| Type: | Postgraduate Subject |
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| Code: | DTSC71-307 |
| Faculty: | Bond Business School |
| Credit: | 10 |
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Description
This subject extends the investigation of modern statistical modelling techniques initiated in Statistical Learning and Regression Models. Topics include models for correlated data including spatial and mixed-effects models, as well as Bayesian hierarchical models including discussion of Markov chain Monte Carlo (MCMC) techniques for calculating posterior estimates, and modern applied re-sampling methods for developing robust measures of model accuracy. The programming language R will be used in this subject.
Subject details
Learning outcomes
- Demonstrate knowledge of the statistical issues associated with correlated data and random effects.
- Evaluate and choose between a variety of advanced statistical model structures.
- Diagnose key aspects of data structures to assess the need for correlational(?) or hierarchical structure.
- Apply the Bayesian modelling framework, including the use of Markov chain Monte Carlo techniques to find posterior estimates.
- Apply resampling methods to develop robust measures of model accuracy.
- Explain the meaning and importance of the results of statistical models with random effects and hierarchical structure.
Enrolment requirements
| Requisites: |
Pre-requisites:Co-requisites:There are no co-requisites |
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| 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.
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| Restrictions: |
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