Students

STAT8178 – Modern Computational Statistical Methods

2021 – Session 1, Special circumstances

Notice

As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group activities on campus, and most will keep an online version available to those students unable to return or those who choose to continue their studies online.

To check the availability of face-to-face and online activities for your unit, please go to timetable viewer. To check detailed information on unit assessments visit your unit's iLearn space or consult your unit convenor.

General Information

Download as PDF
Unit convenor and teaching staff Unit convenor and teaching staff Convener / Lecturer
Benoit Liquet-Weiland
Contact via Email
Room 630, 12 Wally’s Walk
TBA
Hassan Doosti
Credit points Credit points
10
Prerequisites Prerequisites
(STAT806 or STAT810 or STAT8310 or STAT6110) or (Admission to MBusAnalytics and BUSA8000 and ECON8040)
Corequisites Corequisites
Co-badged status Co-badged status
STAT7178
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, penalised likelihood, 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. State-of-the-art computing softwares are used.

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

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Derive and explore Maximum Likelihood and Penalised Maximum Likelihood estimators.
  • ULO2: Apply EM algorithm to deal with missing data.
  • ULO3: Produce estimates of bias and variance along with confidence interval by applying Monte-Carlo and bootstrappping methods.
  • ULO4: Apply nonparametric function estimation approaches to estimate density function, regression function and quantile regression function.
  • ULO5: Evaluate the performance of nonparametric curve estimators by applying Monte-Carlo and bootstrapping methods.

Assessment Tasks

Name Weighting Hurdle Due
Assignment 1 20% No Week 4
Mid-Semester Test 10% No Week 8
Assignment 2 20% No Week 10
Final Exam 50% No TBA

Assignment 1

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 10 hours
Due: Week 4
Weighting: 20%

 

Students will write code and interpret output in order to answer statistical questions. Students may work on the assignment on their own computers or using University resources.

 


On successful completion you will be able to:
  • Derive and explore Maximum Likelihood and Penalised Maximum Likelihood estimators.
  • Apply EM algorithm to deal with missing data.
  • Produce estimates of bias and variance along with confidence interval by applying Monte-Carlo and bootstrappping methods.

Mid-Semester Test

Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 1 hours
Due: Week 8
Weighting: 10%

 

Mid-Semester test

 


On successful completion you will be able to:
  • Derive and explore Maximum Likelihood and Penalised Maximum Likelihood estimators.
  • Apply EM algorithm to deal with missing data.

Assignment 2

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 10 hours
Due: Week 10
Weighting: 20%

 

Students will write code and interpret output in order to answer statistical questions. Students may work on the assignment on their own computers or using University resources.

 


On successful completion you will be able to:
  • Apply nonparametric function estimation approaches to estimate density function, regression function and quantile regression function.
  • Evaluate the performance of nonparametric curve estimators by applying Monte-Carlo and bootstrapping methods.

Final Exam

Assessment Type 1: Examination
Indicative Time on Task 2: 2 hours
Due: TBA
Weighting: 50%

 

Formal invigilated examination testing the learning outcomes of the unit.

 


On successful completion you will be able to:
  • Derive and explore Maximum Likelihood and Penalised Maximum Likelihood estimators.
  • Apply EM algorithm to deal with missing data.
  • Produce estimates of bias and variance along with confidence interval by applying Monte-Carlo and bootstrappping methods.
  • Apply nonparametric function estimation approaches to estimate density function, regression function and quantile regression function.
  • Evaluate the performance of nonparametric curve estimators by applying Monte-Carlo and bootstrapping methods.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • the Writing Centre for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation

Delivery and Resources

You should attend the following classes each week:

· one 2 hour synchronous online lecture

· one 1 hour SGTA online class

Check timetables.mq.edu.au or the unit iLearn page for class details.

Lectures begin in Week 1. Lecture notes are available on iLearn prior to the lecture.

SGTA  classes begin in week 2 and are based on work from the current week’s lecture. 

 

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:

  • Pattern Recognition and Machine Learning, Bishop, Christopher M. 2006.
  • Machine Learning: A Probabilistic Perspective, Kevin Murphy, MIT Press, 2012.
  • 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 R through Rstudio and MATLAB  in teaching this unit. R and Rstudio are free software and are widely used nowadays by statisticians. Matlab is commercial software, but is available for Macquarie students and staff: https://web.science.mq.edu.au/it/matlab/.

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/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 globalmba.support@mq.edu.au

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Learning Skills

Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to help you improve your marks and take control of your study.

The Library provides online and face to face support to help you find and use relevant information resources. 

Student Services and Support

Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.

Student Enquiries

For all student enquiries, visit Student Connect at ask.mq.edu.au

If you are a Global MBA student contact globalmba.support@mq.edu.au

IT Help

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 information based on version 2021.02 of the Handbook