STAT878
Modern Computational Statistical Methods
S1 Evening 2019
Dept of Mathematics and Statistics
Contents
General Information 2
Learning Outcomes 3
Assessment Tasks 3
Delivery and Resources 6
Unit Schedule 7
Policies and Procedures 7
Graduate Capabilities 9
Changes from Previous Offering 11
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measures to ensure the information in this
publication is accurate and up-to-date. However,
the information may change or become out-dated
as a result of change in University policies,
procedures or rules. The University reserves the
right to make changes to any information in this
publication without notice. Users of this
publication are advised to check the website
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this publication.
Disclaimer
https://unitguides.mq.edu.au/unit_offerings/104951/unit_guide/print 1
General Information
Important Academic Dates
Information about important academic dates including deadlines for withdrawing from units are
available at https://www.mq.edu.au/study/calendar-of-dates
Unit convenor and teaching staff
Unit Convenor
Thomas Fung
Contact via Email
12 Wally's Walk Office 6.26
Tuesday 10am - 12noon
Lecturer
Hassan Doosti
Contact via Email
12 Wally's Walk Office 5.34
Wednesday 10am - 12 noon
Hassan Doosti
Credit points
4
Prerequisites
Corequisites
((Admission to MAppStat or GradCertAppStat or GradDipAppStat or MActPrac or MDataSc or
MSc) and (STAT806 or STAT810)) or (admission to MInfoTech)
Co-badged status
Co-badged with STAT778.
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.
Unit guide STAT878 Modern Computational Statistical Methods
https://unitguides.mq.edu.au/unit_offerings/104951/unit_guide/print 2
Learning Outcomes
On successful completion of this unit, you will be able to:
Ability to compute maximum likelihood and Bayesian estimates
Ability to make inferences using these estimates
Know how to deal with missing data and use the EM algorithm
Compute nonparametric estimators of probability density function
Compute nonparametric estimators of regression function and smoothed quantile
regression
Understand Monte-Carlo inferential statistics and understand bootstrappping estimates
of bias, variance and CI computations
Gain proficiency in Matlab and R
Assessment Tasks
Name Weighting Hurdle Due
Assignment 1 20% No week 4
Mid-Semester Test 10% No week 7
Assignment 2 20% No week 10
Final exam 50% No Formal Examination Period
Assignment 1
Due: week 4
Weighting: 20%
Assignments must be completed individually and submitted via iLearn. Discussions are allowed
but the final work must be your personal effort. Assignments should be word-processed.
All assignments and assessment tasks must be submitted by the official due date and
time. No marks will be given for late work unless an extension has been granted following
a successful application for Special Consideration. Please contact the unit convenor for
advice as soon as you become aware that you may have difficulty meeting any of the
assignment deadlines.
On successful completion you will be able to:
• Ability to compute maximum likelihood and Bayesian estimates
• Ability to make inferences using these estimates
• Know how to deal with missing data and use the EM algorithm
Unit guide STAT878 Modern Computational Statistical Methods
https://unitguides.mq.edu.au/unit_offerings/104951/unit_guide/print 3
• Understand Monte-Carlo inferential statistics and understand bootstrappping estimates
of bias, variance and CI computations
• Gain proficiency in Matlab and R
Mid-Semester Test
Due: week 7
Weighting: 10%
During Week 7, a test will be made available on the iLearn site of the unit. The test's due date is
Friday 11.59pm in week 7. Students are allowed a maximum of two hours and one attempt to
complete the test until the deadline.
The only excuse for not completing the mid-semester test at the designated time period is
because of documented illness or unavoidable disruption. In these special circumstances
you may apply for special consideration via ask.mq.edu.au.
On successful completion you will be able to:
• Ability to compute maximum likelihood and Bayesian estimates
• Ability to make inferences using these estimates
• Gain proficiency in Matlab and R
Assignment 2
Due: week 10
Weighting: 20%
Assignments must be completed individually and submitted via iLearn. Discussions are allowed
but the final work must be your personal effort. Assignments should be word-processed.
All assignments and assessment tasks must be submitted by the official due date and
time. No marks will be given for late work unless an extension has been granted following
a successful application for Special Consideration. Please contact the unit convenor for
advice as soon as you become aware that you may have difficulty meeting any of the
assignment deadlines.
On successful completion you will be able to:
• Compute nonparametric estimators of probability density function
• Compute nonparametric estimators of regression function and smoothed quantile
regression
• Understand Monte-Carlo inferential statistics and understand bootstrappping estimates
of bias, variance and CI computations
• Gain proficiency in Matlab and R
Unit guide STAT878 Modern Computational Statistical Methods
https://unitguides.mq.edu.au/unit_offerings/104951/unit_guide/print 4
Final exam
Due: Formal Examination Period
Weighting: 50%
There will be a 2-hour (supervised) exam during the University Examination Period. The second
half of the semester will be more emphasised (because the first half will have been tested in the
mid-semester test), but the entire unit will be considered examinable in this exam.
Students are expected to present themselves for examination at the time and place designated
in the University Examination Timetable. The timetable will be available in Draft form
approximately eight weeks before the commencement of the examinations and in Final form
approximately four weeks before the commencement of the examinations.
You are advised that it is Macquarie University policy not to set early examinations for individuals
or groups of students. All students are expected to ensure that they are available until the end of
the teaching semester, that is, the final day of the official examination period.
The only excuse for not sitting an examination at the designated time is because of documented
illness or unavoidable disruption. In these special circumstances you may apply for special
consideration via ask.mq.edu.au.
If you receive special consideration for the final exam, a supplementary exam will be scheduled
in the interval between the regular exam period and the start of the next session. By making a
special consideration application for the final exam you are declaring yourself available for a resit
during the supplementary examination period and will not be eligible for a second special
consideration approval based on pre-existing commitments. Please ensure you are familiar with
the policy prior to submitting an application. You can check the supplementary exam information
page on FSE101 in iLearn (bit.ly/FSESupp) for dates, and approved applicants will receive an
individual notification one week prior to the exam with the exact date and time of their
supplementary examination.
On successful completion you will be able to:
• Ability to compute maximum likelihood and Bayesian estimates
• Ability to make inferences using these estimates
• Know how to deal with missing data and use the EM algorithm
• Compute nonparametric estimators of probability density function
• Compute nonparametric estimators of regression function and smoothed quantile
regression
• Understand Monte-Carlo inferential statistics and understand bootstrappping estimates
of bias, variance and CI computations
• Gain proficiency in Matlab and R
Unit guide STAT878 Modern Computational Statistical Methods
https://unitguides.mq.edu.au/unit_offerings/104951/unit_guide/print 5
Delivery and Resources
Lectures
You are required to attend one 3-hour combined lecture and practical class each week. Please
consult the university timetables for the exact time and location of the class.
Prescribed texts
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:
• Computational Statistics Handbook with MATLAB®, W. L. Martinez and A. R. Martinez,
Chapman & Hall. (QA276.4.M272)
• Local regression and likelihood, C. Loader, Springer-Verlag, 1999. QA276.8 .L6/1999.
• Quantile Regression, Roger Koenker, Cambridge University Press 2005,
Unit webpage
Unit webpage is located on iLearn at https://ilearn.mq.edu.au.
You can only access the material on iLearn if you are formally enrolled in the unit. All lecturing
materials are available at this webpage.
Teaching and Learning Strategy
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 on time via iLearn;
· participate the mid-semester test at the designated time;
· contact the unit convenor in advance if for any reason, you cannot hand in your
assessment tasks on time;
Refer to the next section for a week-by-week list of topics to be covered in this unit.
Software used in teaching
We are using MATLAB, R and JAGS/WinBUGS in teaching this unit. R, JAGS/WinBUGS are
free software and are widely used nowadays by statisticians. More information about R can be
Unit guide STAT878 Modern Computational Statistical Methods
https://unitguides.mq.edu.au/unit_offerings/104951/unit_guide/print 6
Unit Schedule
Policies and Procedures
found at http://www.r-project.org/, JAGS at "http://mcmc-jags.sourceforge.net" and WinBUGS at
“http://www.mrc-bsu.cam.ac.uk/bugs/”. Matlab is commercial software, but is available for
Macquarie students and staff: https://web.science.mq.edu.au/it/matlab/.
Week Topic
1 Likelihood and maximum likelihood estimates (MLE)
2 Iterative methods for computing MLE
3 Iterative methods for computing MLE (cont.) & Prior and posterior distributions
4 Prior and posterior distributions (cont.) & Bayesian Estimation
5 Asymptotic distribution: ML & Bayesian Estimates
6 Missing data mechanism, incomplete data and its inference and the Expectation and Maximisation (EM) algorithm
7 Histogram & density estimation
8 Kernel density estimation
9 Kernel regression
10 Quantile regression
11 Monte-Carlo method for hypothesis testing
12 Bootstrapping
13 (Self) Revision
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 (https://staff.m
q.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-centr
al). Students should be aware of the following policies in particular with regard to Learning and
Teaching:
• Academic Appeals Policy
• Academic Integrity Policy
• Academic Progression Policy
• Assessment Policy
• Fitness to Practice Procedure
• Grade Appeal Policy
Unit guide STAT878 Modern Computational Statistical Methods
https://unitguides.mq.edu.au/unit_offerings/104951/unit_guide/print 7
Student Support
Student Services and Support
Student Enquiries
• Complaint Management Procedure for Students and Members of the Public
• Special Consideration Policy (Note: The Special Consideration Policy is effective from 4
December 2017 and replaces the Disruption to Studies Policy.)
Undergraduate students seeking more policy resources can visit the Student Policy Gateway (htt
ps://students.mq.edu.au/support/study/student-policy-gateway). It is your one-stop-shop for the
key policies you need to know about throughout your undergraduate student journey.
If you would like to see all the policies relevant to Learning and Teaching visit Policy Central (http
s://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/p
olicy-central).
Student Code of Conduct
Macquarie University students have a responsibility to be familiar with the Student Code of
Conduct: https://students.mq.edu.au/study/getting-started/student-conduct
Results
Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) 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 or if you are a Global MBA
student contact [email protected]
Macquarie University provides a range of support services for students. For details, visit http://stu
dents.mq.edu.au/support/
Learning Skills
Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study
strategies to improve your marks and take control of your study.
• Workshops
• StudyWise
• Academic Integrity Module for Students
• Ask a Learning Adviser
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
If you are a Global MBA student contact [email protected]
Unit guide STAT878 Modern Computational Statistical Methods
https://unitguides.mq.edu.au/unit_offerings/104951/unit_guide/print 8
IT Help
Graduate Capabilities
PG - Discipline Knowledge and Skills
Our postgraduates will be able to demonstrate a significantly enhanced depth and breadth of
knowledge, scholarly understanding, and specific subject content knowledge in their chosen
fields.
This graduate capability is supported by:
Learning outcomes
• Ability to compute maximum likelihood and Bayesian estimates
• Ability to make inferences using these estimates
• Know how to deal with missing data and use the EM algorithm
• Compute nonparametric estimators of probability density function
• Compute nonparametric estimators of regression function and smoothed quantile
regression
• Understand Monte-Carlo inferential statistics and understand bootstrappping estimates
of bias, variance and CI computations
• Gain proficiency in Matlab and R
Assessment tasks
• Assignment 1
• Mid-Semester Test
• Assignment 2
• Final exam
PG - Critical, Analytical and Integrative Thinking
Our postgraduates will be capable of utilising and reflecting on prior knowledge and experience,
of applying higher level critical thinking skills, and of integrating and synthesising learning and
knowledge from a range of sources and environments. A characteristic of this form of thinking is
the generation of new, professionally oriented knowledge through personal or group-based
critique of practice and theory.
This graduate capability is supported by:
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.
Unit guide STAT878 Modern Computational Statistical Methods
https://unitguides.mq.edu.au/unit_offerings/104951/unit_guide/print 9
Learning outcomes
• Ability to compute maximum likelihood and Bayesian estimates
• Ability to make inferences using these estimates
• Know how to deal with missing data and use the EM algorithm
• Compute nonparametric estimators of probability density function
• Compute nonparametric estimators of regression function and smoothed quantile
regression
• Understand Monte-Carlo inferential statistics and understand bootstrappping estimates
of bias, variance and CI computations
• Gain proficiency in Matlab and R
Assessment tasks
• Assignment 1
• Mid-Semester Test
• Assignment 2
• Final exam
PG - Research and Problem Solving Capability
Our postgraduates will be capable of systematic enquiry; able to use research skills to create
new knowledge that can be applied to real world issues, or contribute to a field of study or
practice to enhance society. They will be capable of creative questioning, problem finding and
problem solving.
This graduate capability is supported by:
Learning outcomes
• Ability to compute maximum likelihood and Bayesian estimates
• Ability to make inferences using these estimates
• Know how to deal with missing data and use the EM algorithm
• Compute nonparametric estimators of probability density function
• Compute nonparametric estimators of regression function and smoothed quantile
regression
• Understand Monte-Carlo inferential statistics and understand bootstrappping estimates
of bias, variance and CI computations
• Gain proficiency in Matlab and R
Assessment tasks
• Assignment 1
Unit guide STAT878 Modern Computational Statistical Methods
https://unitguides.mq.edu.au/unit_offerings/104951/unit_guide/print 10
• Mid-Semester Test
• Assignment 2
• Final exam
Changes from Previous Offering
The Take-home Exam has been replaced by some new assessment tasks.
Unit guide STAT878 Modern Computational Statistical Methods
https://unitguides.mq.edu.au/unit_offerings/104951/unit_guide/print 11
