Statistical Inference

Subject 620-620 (2009)

Note: This is an archived Handbook entry from 2009. Search for this in the current handbook

Credit Points: 12.50
Level: 9 (Graduate/Postgraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2009:

Semester 1, - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period not applicable
Assessment Period End not applicable
Last date to Self-Enrol not applicable
Census Date not applicable
Last date to Withdraw without fail not applicable


Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: 36 hours comprising one two-hour lecture per week and one one-hour practical class.
Total Time Commitment: Not available
Prerequisites: None
Corequisites: None
Recommended Background Knowledge:

It is recommended that students have completed an undergraduate subject in statistics (such as 620-202 [2008] Statistics or its equivalent) and have either taken or be concurrently enrolled in Probability for Inference from the present program.

Non Allowed Subjects: None
Core Participation Requirements:

It is University policy to take all reasonable steps to minimise the impact of disability upon academic study and reasonable steps will be made to enhance a student's participation in the University's programs. This subject requires all students to actively and safely participate in laboratory activities. Students who feel their disability may impact upon their participation are encouraged to discuss this with the subject coordinator and the Disability Liaison Unit.

Coordinator

Prof Richard Mark Huggins
Subject Overview: Classical statistics is concerned with parametric models, which are idealized versions of reality that allow the development of an elegant mathematical theory of inference. Modern Statistics develops methods that weaken the assumptions of these classical methods. In this course we review classical statistical methods and then consider their generalisation using estimating equations. Topics include: Review of Classical Inference. Properties of Maximum Likelihood Estimators. Hypothesis Testing & Model Selection. Generalized Linear Models. The EM Algorithm. Optimal Estimating Equations, Quasi likelihood, Generalised Estimating Equations.
Objectives:

After completing this subject students should gain:

  • an understanding of the classical theory of statistics and how it has developed into modern statistics;
  • an understanding of the role of estimating equations in statistics;
  • the ability to pursue further studies in this and related areas.
Assessment:

Up to 40 pages of written assignments (20%: two assignments worth 10% each, due mid and late in semester), a three-hour written examination (80%, in the examination period).

Prescribed Texts: None.
Recommended Texts:

Davison, A.C. (2003) Statistical Models.

Casella, G & Berger, R.L. (2002) Statistical Inference.

Breadth Options:

This subject is not available as a breadth subject.
Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills:

Upon completion of this subject, students should gain:

  • problem-solving skills including engaging with unfamiliar problems and identifying relevant strategies;
  • analytical skills -- the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of an analysis; and
  • through interactions with other students, the ability to work in a team.
Related Majors/Minors/Specialisations: R05 RM Master of Science - Mathematics and Statistics

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