Unit convenor and teaching staff |
Unit convenor and teaching staff
Unit Convenor
Jun Ma
Contact via jun.ma@mq.edu.au
Room 5.26, 12 Wally's Walk
Wed 5 - 6pm
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Credit points |
Credit points
4
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Prerequisites |
Prerequisites
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Corequisites |
Corequisites
((Admission to MAppStat or GradDipAppStat or MActPrac) and (STAT806 or STAT810)) or (admission to MSc or MInfoTech)
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Co-badged status |
Co-badged status
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Unit description |
Unit description
This unit offers students the opportunity to study some modern computational methods in statistics. The first half of the unit covers maximum likelihood computations, Bayesian computations using Monte Carlo methods, missing data and the EM algorithm. The second half considers Kernel density estimation, Kernel regression, quantile regression and inferences using Monte-Carlo and bootstrapping methods. The computing software MATLAB, R and WinBUGS are used.
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Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
Name | Weighting | Hurdle | Due |
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Assignment 1 | 20% | No | week 6 lecture |
Assignment 2 | 20% | No | week 13 lecture |
Take home exam | 30% | No | 10am, June 12 |
Written exam | 30% | No | TBA |
Due: week 6 lecture
Weighting: 20%
This assignment covers weeks 1 - 6 materials. Assignments comprise a major part of the learning process. Late submissions without approval will be penalized at a rate of 20% deduction per day. Assignments must be each student’s own work. Discussions are allowed but the final work must be your personal effort. We prefer that assignments are word-processed.
Due: week 13 lecture
Weighting: 20%
This assignment covers weeks 7 - 12 materials. For policy on later submission and other issues please see the Assignment 1 description.
Due: 10am, June 12
Weighting: 30%
This take home exam covers the teaching materials from week 1 to week 13 and it will be available on iLearn from 10am on Friday 9 June 2017. Your answers to this exam must be submitted electronically (by email) to A/Prof Jun Ma by 10am Monday 12 June 2017. Your answers should be word processed. Matlab/R and WinBUGS codes written to answer the exam questions should also be included as an attachment. This take home exam must be submitted on time. Any later submissions without prior approval will NOT be accepted.
Due: TBA
Weighting: 30%
This is a 2-hour supervised exam and it will cover the lecture materials from week 1 to week 13. Its date will be within the university Examination Period.
LECTURES
You are required to attend a 3-hour lecture (and practice) each week; the time and room are:
Wednesday 6.00 – 9.00pm E4B 306 Faculty PC Lab
Students should obtain the lecture overheads from iLearn prior to the lecture. The lecture overheads are available module by module.
The following are recommended reading books for this unit
Unit webpage is located on Moodle at https://ilearn.mq.edu.au.
You can only access the material on Moodle if you are enrolled in the unit. All lecturing materials are available at this webpage.
The unit is taught in both traditional mode and external mode. In traditional mode, students are on campus in standard semesters with weekly lectures. In external mode, students access all teaching material from iLearn and do not attend lectures on campus.
Students are expected to
· attend all the lectures if enrolled internally;
· have read through the material to be covered using the lecture notes provided on iLearn;
· submit assignments due in weeks 6 and 12 to the appropriate lecturer;
· contact the unit convenor in advance if for any reason, you cannot hand in your assessment tasks on time;
· collect their marked assessment from the lecturer during the lecture if enrolled internally. External students will have their marked assessment sent to them.
Refer to end of this handout for a week-by-week list of topics to be covered in this unit.
EXAMINATIONS
If you notify the University of your disruption to studies for your final examination, you must make yourself available for the week of July 24 – 28, 2017. If you are not available at that time, there is no guarantee an additional examination time will be offered. Specific examination dates and times will be determined at a later date.
SOFTWARE USED IN TEACHING
We are using MATLAB (or R) and WinBUGS in teaching this unit. R and WinBUGS are free software and are widely used nowadays by statisticians. More information about R can be found at http://www.r-project.org/, and WinBUGS at “http://www.mrc-bsu.cam.ac.uk/bugs/”.
CHANGES FROM PREVIOUS OFFERINGS
None
None
Unit Schedule
Week |
Topic |
Software |
Assignment |
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Out |
Due |
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1 |
Likelihood and maximum likelihood estimates (MLE) |
Matlab |
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2 |
Iterative methods for computing MLE |
Matlab |
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3 |
Iterative methods for computing MLE (cont.) Prior and posterior distributions |
Matlab |
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4 |
Prior and posterior distributions (cont.) Bayesian estimates Bayesian computation: posterior mean Bayesian computation: posterior mode WinBUGS |
Matlab, WinBUGS |
Ass 1 |
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5 |
Asymptotic distribution: MLE Asymptotic distribution: posterior mode |
Matlab |
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6 |
Missing data mechanism Complete data and incomplete data Inference based on incomplete data The EM algorithm |
Matlab |
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Ass 1 |
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7 |
Histogram & density estimation
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Matlab
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8 |
Kernel density estimation |
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9 |
Kernel regression |
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10 11 |
Quantile regression Monte-Carlo method for inferential statistics Basic procedure Monte-Carlo hypothesis testing |
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Ass 2 |
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12
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Bootstrap methods Bootstrap method of bias Bootstrap estimate of variance Bootstrap confidence intervals Review |
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13 |
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Ass 2 |
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Students should read the lecture notes, which will be available at the unit web page, before the lecture.
Macquarie University policies and procedures are accessible from Policy Central. Students should be aware of the following policies in particular with regard to Learning and Teaching:
Academic Honesty Policy http://mq.edu.au/policy/docs/academic_honesty/policy.html
Assessment Policy http://mq.edu.au/policy/docs/assessment/policy_2016.html
Grade Appeal Policy http://mq.edu.au/policy/docs/gradeappeal/policy.html
Complaint Management Procedure for Students and Members of the Public http://www.mq.edu.au/policy/docs/complaint_management/procedure.html
Disruption to Studies Policy (in effect until Dec 4th, 2017): http://www.mq.edu.au/policy/docs/disruption_studies/policy.html
Special Consideration Policy (in effect from Dec 4th, 2017): https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policies/special-consideration
In addition, a number of other policies can be found in the Learning and Teaching Category of Policy Central.
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/support/student_conduct/
Results shown in iLearn, or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au.
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to improve your marks and take control of your study.
Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.
For all student enquiries, visit Student Connect at ask.mq.edu.au
For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/.
When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.
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