• Class Number 2676
• Term Code 3030
• Class Info
• Unit Value 6 units
• Mode of Delivery In Person
• COURSE CONVENER
• Dr Anton Westveld
• LECTURER
• Dr Anton Westveld
• Class Dates
• Class Start Date 24/02/2020
• Class End Date 05/06/2020
• Census Date 08/05/2020
• Last Date to Enrol 02/03/2020
SELT Survey Results

Statistical Inference (STAT3013)

This course introduces students to the basic theory behind the development and assessment of statistical analysis techniques in the areas of point and interval estimation, as well as hypothesis testing.  Topics include:
* Point estimation methods, including method of moments and maximum likelihood, bias and variance, mean-squared error, sufficiency, completeness, exponential families, the Cramer-Rao inequality, the Rao-Blackwell theorem, uniformly minimum variance unbiased estimators, and Bayesian estimation methods.
* Confidence interval construction methods, including likelihood-based intervals, inversion methods, intervals based on pivots, Bayesian credible and highest posterior density regions, and resampling based intervals.
* Hypothesis testing methods, including likelihood ratio tests, the Neymann-Pearson lemma and uniformly most powerful tests, power calculations, Bayesian approaches, and non-parametric approaches.

## Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

1. Explain the notion of a parametric model and point estimation of the parameters of those models;
2. Explain and apply approaches to include a measure of accuracy for estimation procedures and our confidence;
3. Assess the plausibility of pre-specified ideas about the parameters of a model by examining the area of hypothesis testing;
4. Explain and apply non-parametric statistics; and,
5. Discuss the computational issues related to the implementation of various statistical inferences.

## Research-Led Teaching

The topic of statistical inference seeks to provide answers to questions of point estimation, interval estimation, and hypothesis testing, based on observable data. Not surprisingly, through the development of this topic over the past couple centuries, there exist diverse approaches to these problems. Examination and application of these diverse approaches will provide insight into the past and potentially future development of statistical science.

## Examination Material or equipment

The permitted material for the exams will be:

• Two sheets of A4 paper with notes on both sides

• Paper-based dictionary (translation dictionary or an English language dictionary), no approval required (must be clear of ALL annotations)

## Required Resources

Paul Garthwaite, Ian Joliffe, and Byron Jones (2009). Statistical Inference (second edition). Oxford Science Publications. A copy of this text will be placed in the library on 2 hour loan. Additionally, this textbook is available from the Harry Harthog bookstore.

Some examples provided in lectures, tutorials, and work related to the tutorials and project will entail the use of the statistical computer packages R and RStudio, which are freely available at www.r-project.org and www.rstudio.com. The R code used for examples provided in lectures, workshops, and tutorials will be available on the course Wattle site. Note: students will not be able to use any statistical package during the exam.

G. Casella and R. Berger (2002). Statistical Inference (second edition). Cengage Learning.

G. Givens and J. Hoeting (2013). Computational Statistics. Wiley.

J. Kadane (2011). Principles of Uncertainty. CRC Press. (pdf)

C. Robert (2001). The Bayesian Choice. Springer.

S. Stern. A set of notes previously used for this course by Professor Steven Stern will be placed on Wattle. ANU

The textbooks above are available from the ANU library.

## Staff Feedback

Students will be given feedback in the following forms in this course:

• Written comments, both individually as well as to the whole class.

• Verbal comments to the whole class.

## Student Feedback

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.

## Other Information

Scaling

Your final mark for the course will be based on the raw marks allocated for each of your assess- ment items. However, your final mark may not be the same number as produced by that formula, as marks may be scaled. Any scaling applied will preserve the rank order of raw marks (i.e. if your raw mark exceeds that of another student, then your scaled mark will exceed the scaled mark of that student), and may be either up or down.

Referencing Requirements

Appropriate referencing will be necessary for the presentations. For more information see: http :

//www.anu.edu.au/students/learning - development/academic - integrity/how - referencing -works

Support for Students

The University offers a number of support services for students. Information on these is available online from http : //students.anu.edu.au/studentlife/

Extensions and Penalties

No submission of assessment tasks without an extension after the due date will be permitted. If an assessment task is not submitted by the due date, a mark of 0 will be awarded.

Co-teaching

The courses STAT3013, STAT4027, and STAT8027 are co-taught.

## Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction
2 Properties of estimators
3 Properties of estimators Tutorial questions
4 Maximum likelihood and other methods of estimation Tutorial questions
5 Maximum likelihood and other methods of estimation Quiz
6 Hypothesis testing Mid-semester exam (either week 6 or week 7)
7 Hypothesis testing Mid-semester exam (either week 6 or week 7)
8 Interval estimation Tutorial questions
9 Bayesian inference & decision theory Tutorial questions
10 Bayesian inference & decision theory Tutorial questions
11 Non-parametric methods
12 Non-parametric methods Project

## Tutorial Registration

Tutorial signup for this course will be done via the Wattle website. Detailed information about signup times will be provided on Wattle or during your first lecture. When tutorials are available for enrolment, follow these steps:

1.   Log on to Wattle, and go to the course site

2.   Click on the link ‘Tutorial enrolment’

3.   On the right of the screen, click on the tab ‘Become Member of . . . .’ for the tutorial class you wish to enter

If you need to change your enrolment, you will be able to do so by clicking on the tab ‘Leave group. . . .’ and then re-enrol in another group. You will not be able to enrol in groups that have reached their maximum number. Please note that enrolment in ISIS must be finalised for you to have access to Wattle.

## Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Compulsory Tutorial Questions 5 % 09/03/2020 31/05/2020 1-5
Quiz 0 % 23/03/2020 30/03/2020 1
Mid-Semester Examination 20 % * * 1,3
Compulsory Presentation/Project 15 % 25/05/2020 12/06/2020 1-5
Compulsory Final Exam 60 % * * 1-5

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details

## Policies

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:

## Assessment Requirements

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 ANU Online 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.

## Participation

Attendance and participation in lectures, workshops, and tutorials is recommended but not assessable.

## Examination(s)

As further academic integrity control, students may be selected for a 15 minute individuals 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.

Value: 5 %
Due Date: 09/03/2020
Return of Assessment: 31/05/2020
Learning Outcomes: 1-5

Compulsory Tutorial Questions

Before five of the weekly tutorial sessions, at the beginning of those weeks (see the class overview and Wattle for the exact date and time), you will submit your answers to tutorial questions online via Wattle. These will be graded for “performance” (whether you reasonably demonstrated the concepts) and not whether you got the answer correct. Each week the “performance” will be graded as 0 or 100. Students may be asked to present their solutions during tutorial.

Value: 0 %
Due Date: 23/03/2020
Return of Assessment: 30/03/2020
Learning Outcomes: 1

Quiz

There will be an online Wattle quiz which will be available at the beginning of Week 5. The quiz will be available for one week. The results will be made available at the beginning of Week 6.

Value: 20 %
Learning Outcomes: 1,3

Mid-Semester Examination

The exact coverage of the exam will be made known at least one week before the examination and will be discussed in class, as well as posted on Wattle. The exam will be held in either Week 6 or Week 7. The exact date will be made available by the University by or during Week 5. The exam will be two hours long and is redeemable. Therefore, it will be worth either 20% of the total assessment, or 0% depending on your final examination score. The grades will be returned two weeks after the exam (not including non-teaching weeks).

Value: 15 %
Due Date: 25/05/2020
Return of Assessment: 12/06/2020
Learning Outcomes: 1-5

Compulsory Presentation/Project

In groups of 2-5 (exact size TBD), based on your cohort (STAT3013 or STAT4027/STAT8027), you will read and present an academic paper. In addition, you will have to consider some type of “extension”. This may be by simplifying the problem and considering another estimator and its properties, extending the inferential method, or even considering applying the method to other data sets. Each presentation will last 10-25 minutes (TBD) and each member of the group must speak. Students will give their presentations via video using the One Button Studio at the ANU library. Your presentation materials and video must be submitted on Wattle by the beginning of Week 12. I will have some possible papers on Wattle or you may choose your own. All paper choices and groups must be approved by the lecturer.

Value: 60 %
Learning Outcomes: 1-5

Compulsory Final Exam

The exam will be during the final examination period and will be 3 hours in length. The exact coverage of the exam will be made known in week 12 at the latest and will be discussed in lecture, as well as posted on Wattle. The exam is worth either 60% of the total assessment, or 80% if the mid-semester exam is redeemed. The exact date will be made available by the University by or during Week 5.

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.

## Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.

## Hardcopy Submission

For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.

## Late Submission

No submission of assessment tasks without an extension after the due date will be permitted. If an assessment task is not submitted by the due date, a mark of 0 will be awarded.

## Referencing Requirements

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.

## Returning Assignments

Marked tutorial questions and the project will be returned online.

## 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

It will not be possible to resubmit either the tutorial questions or the project.

## Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

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).

## Convener

 Dr Anton Westveld 6125 5122 anton.westveld@anu.edu.au

### Research Interests

Research interests include Bayesian methodology and theory, statistical methods for interaction/relational data (network, game theoretic, computer simulation), statistical applications in social (economics, political science, public policy), environmental, and biological sciences.

### Dr Anton Westveld

 By Appointment

## Instructor

 Dr Anton Westveld 6125 5122 anton.westveld@anu.edu.au

### Dr Anton Westveld

 By Appointment