- Class Number 7644
- Term Code 3060
- Class Info
- Unit Value 6 units
- Mode of Delivery In Person
- Prof Alan Welsh
- Prof Alan Welsh
- Class Dates
- Class Start Date 27/07/2020
- Class End Date 30/10/2020
- Census Date 31/08/2020
- Last Date to Enrol 03/08/2020
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.
The main reference will be
Welsh, A.H. (1996). Aspects of Statistical Inference. New York: Wiley.
The ANU Library has been requested to order 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). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.
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 make some use of 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|
|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|
Please see Wattle for tutor's information.
|Assessment task||Value||Learning Outcomes|
|Participation in weekly discussions||10 %||1,2,3,4,5|
|Weekly exercises||40 %||1,2,3,4,5|
|Final Exam||50 %||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 Misconduct Rule before the commencement of their course. Other key policies and guidelines include:
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 Integrity . 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.
10% of the assessment will be for active tutorial participation. See Assessment Task 1.
50% of the assessment will be for the final exam which will be held during the exam period.
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 lecture 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 in-class 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 task should be completed in good english, showing appropriate mathematical working and discussing results where appropriate. Submissions can be neatly handwritten or they can be prepared using mathematical typesetting software such as LATEX.
Assessment Task 3
Learning Outcomes: 1,2,3,4,5
Details of the final assessment with be advised no later than teaching week 10 of the semester. Centrally scheduled examinations through Examinations, Graduations & Prizes will be timetabled prior to the examination period. Please check ANU Timetabling for further information. The examination will cover the entire course content.
Academic integrity is a core part of the ANU culture as a community of scholars. At its heart, academic integrity is about behaving ethically, committing to honest and responsible scholarly practice and upholding these values with respect and fairness.
The ANU commits to assisting all members of our community to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to be familiar with the academic integrity principle and Academic Misconduct Rule, uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with.
The Academic Misconduct Rule is in place to promote academic integrity and manage academic misconduct. Very minor breaches of the academic integrity principle may result in a reduction of marks of up to 10% of the total marks available for the assessment. The ANU offers a number of online and in person services to assist students with their assignments, examinations, and other learning activities. Visit the Academic Skills website for more information about academic integrity, your responsibilities and for assistance with your assignments, writing skills and study.
You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the completed task for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.
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.
Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.
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 Diversity 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