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The great power of the discipline of Statistics is the possibility to make inferences concerning a large population based on optimally learning from increasingly large and complex data. Critical to successful inference is a deep understanding of the theory when the number of samples and the number of observed features is large and require complex statistical methods to be analysed correctly. In this unit you will learn how to integrate concepts from a diverse suite of specialities in mathematics and statistics such as optimisation, functional approximations and complex analysis to make inferences for highly complicated data. In particular, this unit explores advanced topics in statistical methodology examining both theoretical foundations and details of implementation to applications. The unit is made up of distinct modules that may include (but are not restricted to) asymptotic theory for statistics and econometrics, theory and algorithms for statistical learning with big data, and introduction to optimal semiparametric optimality.
Study level | Postgraduate |
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Academic unit | Mathematics and Statistics Academic Operations |
Credit points | 6 |
Prerequisites:
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None |
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Corequisites:
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None |
Prohibitions:
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None |
Assumed knowledge:
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Strong background in probability theory and statistical modelling. Please consult with the coordinator for further information |
At the completion of this unit, you should be able to:
This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.
The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
Session | MoA ? | Location | Outline ? |
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Semester 2 2024
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Normal day | Camperdown/Darlington, Sydney |
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