• Offered by Rsch Sch of Finance, Actuarial Studies & App Stats
  • ANU College ANU College of Business and Economics
  • Classification Advanced
  • Course subject Statistics
  • Areas of interest Statistics, Mathematics
  • Academic career PGRD
  • Course convener
    • Prof Alan Welsh
  • Mode of delivery In Person
  • Co-taught Course
  • Offered in Second Semester 2017
    See Future Offerings

This course is intended to follow on from Statistical Inference (STAT3013/8027) 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:

Upon successful completion of the requirements for this course, students should have the
knowledge and skills to:

• have an in depth understanding of how to carry out maximum likelihood estimation and inference in statistical models with several parameters.  
• be able to apply Taylor series expansions to derive approximate sampling distributions and confidence intervals for vectors of transformed estimators.
• understand the 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.
• have an in depth understanding and be able to explain the different uses of randomisation in statistics.
• have an in depth understanding of the basic principles of statistical inference and the issues they raise about how to do statistical inference.

Other Information

See the course outline on the College courses page. Outlines are uploaded as they become available. 

Indicative Assessment

Typical assessment may include, but is not restricted to: assignments and a final exam.

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 at least 10 hours per week to completing the work in this course. This will include at least 3 contact hours per week and up to 7 hours of private study time.

Requisite and Incompatibility

To enrol in this course you must have completed STAT3013 or STAT8027 Incompatible with STAT3056.

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
Domestic fee paying students
Year Fee
2017 $3660
International fee paying students
Year Fee
2017 $4878
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

The list of offerings for future years is indicative only.
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
8301 24 Jul 2017 31 Jul 2017 31 Aug 2017 27 Oct 2017 In Person N/A

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