- Class Number 5470
- Term Code 3260
- Class Info
- Unit Value 6 units
- Mode of Delivery In Person
- Prof Alan Welsh
- Prof Alan Welsh
- Class Dates
- Class Start Date 25/07/2022
- Class End Date 28/10/2022
- Census Date 31/08/2022
- Last Date to Enrol 01/08/2022
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.
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.
This course draws on the active research interests of the course convenor. These include the directly relevant topics of Statistical Inference, Statistical Modelling, Robustness, Nonparametric and Semi-Parametric methods, Analysis of Sample Surveys.
Examination Material or equipment
Details of the final assessment will be advised no later than teaching week 10 of the semester.
Class materials, including lecture recordings, slides, weekly assignments, weekly tutorials and other relevant materials, will be made available on the class web page on Wattle. It is essential that you visit the class Wattle site regularly. Important: you will need to be correctly enrolled in the course before you can access the Wattle site.
The application of modern statistical techniques requires familiarity with some statistical computing package and some assignments for this course will require some analysis on a computer. This course makes use of the R computing package, which is freely available to download at http://www.r-project.org. Further instructions on R, including a series of revision workshops, will be made available on the Wattle site for this course. R is also available on all InfoCommons computers on the ANU campus.
The main reference will be
Welsh, A.H. (1996). Aspects of Statistical Inference. New York: Wiley.
The ANU Library has the e-book .
Other books on statistical inference in general and on specific topics such as robustness, sample surveys etc may be useful to students.
Students will be given feedback in the following forms in this course:
- written comments
- verbal comments
- feedback to whole class, groups, individuals, focus group etc
ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.
As a further academic integrity control, students may be selected for a 15 minute individual oral examination of their written assessment submissions. Any student identified, either during the current semester or in retrospect, as having used ghost-writing services will be investigated under the University’s Academic Misconduct Rule.
The application of modern statistical techniques requires familiarity with one or more statistical computing packages. In this course we will use the R statistical computing package to do some numerical calculations, to develop examples and to illustrate points made in the lectures. R can be downloaded for free from the internet.
|Week/Session||Summary of Activities||Assessment|
|1||Review of likelihood theory||Workshops and tutorials commence|
|2||Large sample theory|
|3||Likelihood-based confidence intervals and tests|
|4||Introduction to robustness|
|5||Functional calculus and influence functions|
|6||More on robustness|
|8||Randomisation as a basis for inference|
|9||More on randomisation and inference|
|10||The likelihood principle|
|11||Sufficiency and the sufficiency principle|
|12||Ancillarity and the the conditionality principle||Workshops and tutorials end|
Tutorials will be available on campus, live through scheduled Zoom sessions and as pre-recorded videos. Students should enrol in their tutorial using MyTimetable.
"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 (https://www.anu.edu.au/students/program-administration/timetabling)".
|Assessment task||Value||Due Date||Return of assessment||Learning Outcomes|
|Participation in weekly discussions||10 %||25/07/2022||02/11/2022||1,2,3,4,5|
|Weekly exercises||40 %||25/07/2022||28/10/2022||1,2,3,4,5|
|Final exam||50 %||03/11/2022||01/12/2022||1,2,3,4,5|
* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details
ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:
- Academic Integrity Policy and Procedure
- Student Assessment (Coursework) Policy and Procedure
- Special Assessment Consideration Guideline and General Information
- Student Surveys and Evaluations
- Deferred Examinations
- Student Complaint Resolution Policy and Procedure
- Code of practice for teaching and learning
The ANU is using 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. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.
Moderation of Assessment
Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.
Course content delivery will take the form of pre-recorded weekly lectures (available via echo360 on Wattle), 2 hour weekly workshops delivered on campus in person and live through scheduled Zoom sessions, and weekly tutorials delivered in hybrid format (on campus, live through scheduled Zoom sessions and as pre-recorded videos).
Centrally administered examinations through Examinations, Graduations & Prizes will be timetabled prior to the examination period. Please check ANU Timetabling for further information.
Assessment Task 1
Learning Outcomes: 1,2,3,4,5
Participation in weekly discussions
This is a continuous assessment component throughout the semester. Students are expected to attend and contribute to the session (in the scheduled Wednesday workshop session at 1.00 pm) by asking questions and participating in the discussion. In this task, you will be assessed on your demonstration of the required learning outcomes of the course as exhibited through the questions asked and responses given in tutorials. Students will be given regular informal feedback on their performance in this assessment whilst formal marking of this component will be provided to students by the end of Week 6 and 12.
Assessment Task 2
Learning Outcomes: 1,2,3,4,5
A take-home task or an in-class quiz will be set each week. Take-home tasks must be submitted through Turnitin on the following Wednesday by 12.45 pm. As these problems will be discussed in the workshop session starting at 1.00 pm on Wednesday, no late submissions will be accepted. The submitted solutions will be graded and returned by Tuesday in the following week. The submission is required to be clearly written, free from grammatical or punctuation errors. It should show appropriate mathematical working. Submissions can be neatly handwritten or they can be prepared using mathematical typesetting software such as LATEX. Students are encouraged to check that the marks available via the Wattle gradebook are consistent with the marks written on returned quizzes.
Assessment Task 3
Learning Outcomes: 1,2,3,4,5
50% of the assessment will be for the final exam which will be held during the exam period. The examination will cover the entire course content and is to be completed individually. The final examination will be a Wattle-based online exam during the university examination period at the end of semester. The final examination will be two hours long with details of the exam format provided to the students (over Wattle) no later than week 10 of the semester.
Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.
The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.
The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.
The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.
The assignment is to be submitted online on Wattle via Turnitin. You must attach an assignment cover sheet. Please keep a copy of tasks completed for your records.
Online submission only.
Late submission not permitted. Submission of weekly exercises or quizzes without an extension after the due date will result in the award of a mark of 0.
The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.
Weekly exercises or quizzes submitted on Wednesday will be returned by Tuesday in the following week. Quiz marks will be uploaded to the Wattle gradebook feature for the course. It is the responsibility of students to check that these recorded marks are in agreement with the marks written on returned quizzes.
Extensions and Penalties
Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.
Resubmission of Assignments
No weekly exercises or quizzes may be resubmitted.
Distribution of grades policy
Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.
Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.
Support for students
The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).
- ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
- ANU Access and inclusion for students with a disability or ongoing or chronic illness
- ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
- ANU Academic Skills and Learning Centre supports you make your own decisions about how you learn and manage your workload.
- ANU Counselling Centre promotes, supports and enhances mental health and wellbeing within the University student community.
- ANUSA supports and represents undergraduate and ANU College students
- PARSA supports and represents postgraduate and research students
Statistical Inference, Statistical Modelling, Robustness, Nonparametric and Semi-Parametric methods, Analysis of Sample Surveys, Ecological Monitoring.
Prof Alan Welsh
Prof Alan Welsh