All activities that form part of this course will be delivered remotely
This course is intended to follow on from STAT8027 by providing a more advanced treatment of large sample approximation theory and some of its applications to statistical inference. The focus will be on developing a deeper theoretical understanding of some of the important statistical methods by developing the underlying theory. The objectives will be to achieve a deep understanding of particular statistical methods and to learn to use some advanced tools for analyzing and developing statistical methods.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Carry out complex maximum likelihood estimation and inference in statistical models with several parameters;
- Apply Taylor series expansions to derive approximate sampling distributions and confidence intervals for vectors of transformed estimators;
- Discuss in depth concepts of robust estimation in statistics, including the role of influence functions, be able to apply them to evaluate the robustness of estimators and understand how to construct bounded influence robust estimators;
- Explain in detail the different uses of randomisation in statistics; and,
- Discuss and use the principles of statistical inference and the issues they raise about how to do complex statistical inference.
Indicative Assessment
- Students are expected to commit 130 hours of work in completing this course. This includes time spent in scheduled classes and self-directed study time. (100) [LO 1,2,3,4,5]
In response to COVID-19: Please note that Semester 2 Class Summary information (available under the classes tab) is as up to date as possible. Changes to Class Summaries not captured by this publication will be available to enrolled students via Wattle.
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
Students are expected to commit 130 hours of work in completing this course. This includes time spent in scheduled classes and self-directed study time.
Inherent Requirements
Not applicable
Requisite and Incompatibility
Prescribed Texts
Information about the prescribed textbook will be available via the Class Summary.
Specialisations
Fees
Tuition fees are for the academic year indicated at the top of the page.
If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are an undergraduate student and have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). You can find your student contribution amount for each course at Fees. Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2020 | $4050 |
- International fee paying students
| Year | Fee |
|---|---|
| 2020 | $5760 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 7644 | 27 Jul 2020 | 03 Aug 2020 | 31 Aug 2020 | 30 Oct 2020 | In Person | View |
