Skip to menu Skip to content Skip to footer
Course profile

Advanced Mathematical Statistics (STAT3901)

Study period
Sem 1 2025
Location
St Lucia
Attendance mode
In Person

Course overview

Study period
Semester 1, 2025 (24/02/2025 - 21/06/2025)
Study level
Undergraduate
Location
St Lucia
Attendance mode
In Person
Units
2
Administrative campus
St Lucia
Coordinating unit
Mathematics & Physics School

Classical approach to point estimation. Likelihood theory: maximum likelihood (IML), asymptotic theory, nuisance parameters, applications, likelihood ratio test, score tests, Wald tests, exponential family (properties: sufficiency,    completeness). Confidence intervals, hypothesis tests, P-values, and the false discovery rate (FDR). Computational methods including the expectation-maximization (EM) algorithm for calculating ML estimates, the bootstrap method for approximating the sampling distribution of any statistic derived from a random sample, and sampling-based approaches to calculating marginal densities useful in Bayesian analysis. This is the Advanced Science version of STAT3001.

Statistics provides the mathematical language and techniques necessary for understanding and dealing with chance, uncertainty and variability in Nature. In this course you will learn how to use probability and other branches of mathematics to extractᅠ patterns and other useful information from numerical data in a careful and precise manner. The course has twoᅠmain parts:

  • Classical Mathematical Statistics. Here you will learn about the powerful classical statistical techniques for undertaking Statistical Inference on a sound scientificᅠ basis.
  • Computational methods. Here you will learn how modern computational techniques canᅠbe used to implement theᅠrelevant statistical methodology.

Course requirements

Assumed background

The students should have completed a basic introduction to statistics and probability, as in STAT2003 and STAT2004. Knowledge of second-year mathematics, particularly MATH2000/2001, is also desirable, as multivariate integration, differentiation, Taylor expansions, and matrix algebra will be frequently used. Knowledge of Matlab or R programming will be advantageous, as Matlab and/or R will be used for numerical experimentation and analysis.

Prerequisites

You'll need to complete the following courses before enrolling in this one:

(MATH2001 or MATH2901) + (STAT2004 or STAT2904)

Incompatible

You can't enrol in this course if you've already completed the following:

STAT3001

Course contact

Course staff

Lecturer

Timetable

The timetable for this course is available on the UQ Public Timetable.

Additional timetable information

Lectures will be conducted on campus at the timesᅠadvertised in your personal timetable.ᅠ ᅠImportant: If you are ill, then do not attend any classes in person. Alternative arrangements can be organised – consult Blackboard for details.ᅠAll lectures will be recorded and posted on Blackboard.

Practicals will be conducted on campus - consult your personal timetable for times and lcoations.ᅠ ᅠAlternative arrangements will be advertised on Blackboard should the campus be closed for any reason.

Aims and outcomes

The course aims to provide a comprehensive and modern treatment of mathematical statistics, which includes classical subjects such as likelihood, sufficiency, exponential families, delta methods, score intervals, etc, and also addresses computational methods for implementing the theory covered in the first part of the course.

Learning outcomes

After successfully completing this course you should be able to:

LO1.

Understand the main theoretical concepts of mathematical statistics, and use this understanding to undertake statistical inference on a sound scientific basis.

LO2.

Recognize the role of modern computational techniques including those based on Monte Carlo methods and apply them to simple practical problems.

Assessment

Assessment summary

Category Assessment task Weight Due date
Tutorial/ Problem Set Assignments 40%

21/03/2025 2:30 pm

4/04/2025 2:30 pm

9/05/2025 2:30 pm

23/05/2025 2:30 pm

Examination Final examination
  • Hurdle
60%

End of Semester Exam Period

7/06/2025 - 21/06/2025

A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.

Assessment details

Assignments

Mode
Written
Category
Tutorial/ Problem Set
Weight
40%
Due date

21/03/2025 2:30 pm

4/04/2025 2:30 pm

9/05/2025 2:30 pm

23/05/2025 2:30 pm

Task description

There will be 4  assignments during the course. Each assignment is worth 10%.

Submission guidelines

To be handed in via Blackboard

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.

See ADDITIONAL ASSESSMENT INFORMATION for extension/deferral information relating to this assessment item.

Late submission

A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.

You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item then the listed penalty will be deducted per day for up to 7 calendar days, at which point any submission will not receive any marks unless an extension has been approved. Each 24-hour block is recorded from the time the submission is due.

Final examination

  • Hurdle
Mode
Written
Category
Examination
Weight
60%
Due date

End of Semester Exam Period

7/06/2025 - 21/06/2025

Task description

The final examination in this course will be a closed book two hour invigilated exam held during the end-of-semester examination period.

Hurdle requirements

See COURSE GRADING INFORMATION for the hurdle relating to this assessment item.

Exam details

Planning time 10 minutes
Duration 120 minutes
Calculator options

(In person) Casio FX82 series only or UQ approved and labelled calculator

Open/closed book Closed Book examination - no written materials permitted
Exam platform Paper based
Invigilation

Invigilated in person

Submission guidelines

Deferral or extension

You may be able to defer this exam.

See ADDITIONAL ASSESSMENT INFORMATION for the extension and deferred examination information relating to this assessment item.

Course grading

Full criteria for each grade is available in the Assessment Procedure.

Grade Description
1 (Low Fail)

Absence of evidence of achievement of course learning outcomes.

Course grade description: 1-24% The student demonstrates very limited understanding of the theory of the topics listed in the course outline and of the basic concepts in the course material.ᅠ This includes attempts at answering some questions but demonstrating very limited understanding of the key concepts.

2 (Fail)

Minimal evidence of achievement of course learning outcomes.

Course grade description: 25-44% The student demonstrates limited understanding of the theory of the topics listed in the course outline and demonstrates limited knowledge of the techniques used to solve problems.ᅠ This includes attempts at expressing their deductions and explanations and attempts to answer a few questions accurately.

3 (Marginal Fail)

Demonstrated evidence of developing achievement of course learning outcomes

Course grade description: 45-49% or greater than 50% but fails to meet the hurdle requirements. The student demonstrates some understanding of the theory of the topics listed in the course outline and demonstrates a knowledge of the techniques used to solve problems.

4 (Pass)

Demonstrated evidence of functional achievement of course learning outcomes.

Course grade description: 50-64%, subject to the hurdle limitations below. The student demonstrates an understanding of the theory of the topics listed in the course outline and demonstrates a knowledge of the techniques used to solve problems.

5 (Credit)

Demonstrated evidence of proficient achievement of course learning outcomes.

Course grade description: 65-74%, subject to the hurdle limitations below. The student demonstrates a good understanding of the theory of the topics listed in the course outline and can apply the techniques to solve problems.

6 (Distinction)

Demonstrated evidence of advanced achievement of course learning outcomes.

Course grade description: 75-84%, subject to the hurdle limitations below. The student demonstrates a comprehensive understanding of the theory of the topics listed in the course outline and is proficient in applying the techniques to solve both theoretical and practical problems.

7 (High Distinction)

Demonstrated evidence of exceptional achievement of course learning outcomes.

Course grade description: 85-100%,ᅠ subject to the hurdle limitations below. The student demonstrates an excellent understanding of the theory of the topics listed in the course outline and is highly proficient in applying the techniques to solve both theoretical and practical problems.

Additional course grading information

Hurdle

In order to achieve a pass grade (4 or higher) the student needs to obtain 40% of the total marks for the exam.

Supplementary assessment

Supplementary assessment is available for this course.

Supplementary assessment provides an additional opportunity to demonstrate you have achieved all the required learning outcomes for a course.

If you apply and are granted supplementary assessment, the type of supplementary assessment set will consider which learning outcome(s) have not been met.

Supplementary assessment in this course will be a 2-hour examination similar in style to the end- of-semester examination. To receive a passing grade of 3S4, you must obtain a mark of 50% or more on the supplementary assessment.

Additional assessment information

It is your responsibility to check that assessment marks have been correctly entered on Blackboard > My Grades.ᅠ ᅠIf you feel an error has been made in assessing your work, then contact the course co-ordinator.

Artificial Intelligence

Assessment tasks in this course evaluate students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.

Applications for Extensions to Assessment Due Dates

Extension requests are submitted online via my.UQ – applying for an extension. Extension requests received in any other way will not be approved. Additional details associated with extension requests, including acceptable and unacceptable reasons, may be found at my.UQ.

Please note:

  • Requests for an extension to an assessment due date must be submitted through your my.UQ portal and you must provide documentation of your circumstances, as soon as it becomes evident that an extension is needed. Your application must be submitted on or before the assessment item's due date and time.
  • Applications for extension can take time to be processed so you should continue to work on your assessment item while awaiting a decision. We recommend that you submit any completed work by the due date, and this will be marked if your application is not approved. Should your application be approved, then you will be able to resubmit by the agreed revised due date.
  • If an extension is approved, you will be notified via your my.UQ portal and the new date and time for submission provided. It is important that you check the revised date as it may differ from the date that you requested.
  • If the basis of the application is a medical condition, applications should be accompanied by a medical certificate dated prior to the assignment due date. If you are unable to provide documentation to support your application by the due date and time you must still submit your application on time and attach a written statement (Word document) outlining why you cannot provide the documentation. You must then upload the documentation to the portal within 24 hours.
  • If an extension is being sought on the basis of exceptional circumstances, it must be accompanied by supporting documentation (eg. Statutory declaration).
  • For extensions based on a SAP you may be granted a maximum of 7 days (if no earlier maximum date applies). See the Extension or Deferral availability section of each assessment for details. Your SAP is all that is required as documentation to support your application. However, additional extension requests for the assessment item will require the submission of additional supporting documentation e.g., a medical certificate. All extension requests must be received by the assessment due date and time.
  • Students may be asked to submit evidence of work completed to date. Lack of adequate progress on your assessment item may result in an extension being denied.
  • If you have been ill or unable to attend class for more than 14 days, you are advised to carefully consider whether you are capable of successfully completing your courses this semester. You might be eligible to withdraw without academic penalty - seek advice from the Faculty that administers your program.
  • There are no provisions for exemption from an assessment item within UQ rules. If you are unable to submit an assessment piece then, under special circumstances, you may be granted an exemption, but may be required to submit alternative assessment to ensure all learning outcomes are met.

Applications to defer an exam

In certain circumstances you can apply to take a deferred examination for in-semester and end-of-semester exams. You'll need to demonstrate through supporting documentation how unavoidable circumstances prevented you from sitting your exam. If you can’t, you can apply for a one-off discretionary deferred exam.

Deferred Exam requests are submitted online via mySi-net. Requests received in any other way will not be approved. Additional details associated with deferred examinations, including acceptable and unacceptable reasons may be found at my.UQ.

Please note:

  • Applications can be submitted no later than 5 calendar days after the date of the original exam.
  • There are no provisions to defer a deferred exam. You need to be available to sit your deferred examination.
  • Your deferred examination request(s) must have a status of "submitted" in mySI-net to be assessed.
  • All applications for deferred in-semester examinations are assessed by the relevant school. Applications for deferred end-of-semester examinations are assessed by the Academic Services Division.
  • You’ll receive an email to your student email account when the status of your application is updated.
  • If you have a medical condition, mental health condition or disability and require alternative arrangements for your deferred exam you’ll need to complete the online alternative exam arrangements through my.UQ. This is in addition to your deferred examinations request. You need to submit this request on the same day as your request for a deferred exam or supplementary assessment. Contact Student Services if you need assistance completing your alternative exam arrangements request.

Learning resources

You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.

Library resources

Find the required and recommended resources for this course on the UQ Library website.

Additional learning resources information

Additional resources can be found at the Blackboard site: https://learn.uq.edu.au/webapps/login/

Learning activities

The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.

Filter activity type by

Please select
Clear filters
Learning period Activity type Topic
Multiple weeks
Lecture

Lectures

  1. Understand the main theoretical concepts of mathematical statistics, and use this understanding to undertake statistical inference on a sound scientific basis.
  2. Recognize the role of modern computational techniques including those based on Monte Carlo methods and apply them to simple practical problems.


Learning outcomes: L01, L02

Practical

Practicals

  1. Understand the main theoretical concepts of mathematical statistics, and use this understanding to undertake statistical inference on a sound scientific basis.
  2. Recognize the role of modern computational techniques including those based on Monte Carlo methods and apply them to simple practical problems.


Learning outcomes: L01, L02

Policies and procedures

University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:

Learn more about UQ policies on my.UQ and the Policy and Procedure Library.