Unit convenor and teaching staff |
Unit convenor and teaching staff
Xian Zhou
Deanna Tracy
|
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Credit points |
Credit points
10
|
Prerequisites |
Prerequisites
(ACST255 or ACST2055) and (STAT272 or STAT2372)
|
Corequisites |
Corequisites
|
Co-badged status |
Co-badged status
|
Unit description |
Unit description
This unit provides sophisticated statistical and probabilistic models for survival, sickness, insurance losses and other actuarial problems based on survival data. Techniques of survival analysis are used to estimate survival and loss distributions and evaluate risk factors in actuarial applications. Methods of both nonparametric and parametric estimation are utilised. Advanced models based on Markov chains and processes will be introduced to capture the features of stochastic transitions between different survival or loss states and to estimate the transition rates. Methods for valuing cashflows that are contingent upon multiple transition events and methods of projecting and valuing such expected cashflows will also be covered. Students gaining a weighted average of credit across all of ACST3058, ACST3060 and the CS2-related components of the assessment in ACST3059 (minimum mark of 60% on all three components) will satisfy the requirements for exemption from the professional subject CS2 of the Actuaries Institute. |
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:
Late submissions of assessments Unless a Special Consideration request has been submitted and approved, no extensions will be granted. There will be a deduction of 10% of the total available assessment-task marks made from the total awarded mark for each 24-hour period or part thereof that the submission is late. Late submissions will only be accepted up to 96 hours after the due date and time.
No late submissions will be accepted for timed assessments – e.g., quizzes, online tests.
Table 1: Penalty calculation based on submission time
Submission time after the due date (including weekends) |
Penalty (% of available assessment task mark) |
Example: for a non-timed assessment task marked out of 30 |
< 24 hours |
10% |
10% x 30 marks = 3-mark deduction |
24-48 hours |
20% |
20% x 30 marks = 6-mark deduction |
48-72 hours |
30% |
30% x 30 marks = 9-mark deduction |
72-96 hours |
40% |
40% x 30 marks = 12-mark deduction |
> 96 hours |
100% |
Assignment won’t be accepted |
Special Consideration
To request an extension on the due date/time for a timed or non-timed assessment task, you must submit a Special Consideration application. An application for Special Consideration does not guarantee approval.
The approved extension date for a student becomes the new due date for that student. The late submission penalties above then apply as of the new due date.
Name | Weighting | Hurdle | Due |
---|---|---|---|
Class Test | 20% | No | 07 April 2022 |
Assignment | 20% | No | 1 June 2022 |
Final Exam | 60% | No | Examination Period |
Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 17 hours
Due: 07 April 2022
Weighting: 20%
The test will be approximately 90 minutes, to be held during class time.
Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 20 hours
Due: 1 June 2022
Weighting: 20%
Problem-solving questions requiring detailed solutions using the statistical package R.
Assessment Type 1: Examination
Indicative Time on Task 2: 28 hours
Due: Examination Period
Weighting: 60%
The final examination will be a three-hour written paper with ten minutes reading time, to be held during the University Examination period.
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
Classes
Unit Web Page
Technology Used and required
Required and Recommended Texts and/or Materials
Week 1: Probability models (revision); Survival analysis
Week 2: Estimation of survival distributions
Week 3: Variance estimation and confidence intervals
Week 4: Cox proportional hazards models
Week 5: Cox proportional hazards models; Stochastic processes
Week 6: Markov chains
Week 7: Class test; Markov chains
Semester break
Week 8: Markov jump processes
Week 9: Markov jump processes
Week 10: Applications of Markov processes
Week 11: Applications of Markov processes
Week 12: Multi-state insurance models and multiple decrement tables
Week 13: Assignment and Revision
Note: This is only a tentative schedule. The actual schedule will be adjusted from time to time in accordance with the progress of lectures.
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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.
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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
At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
The Writing Centre provides resources to develop your English language proficiency, academic writing, and communication skills.
The Library provides online and face to face support to help you find and use relevant information resources.
Macquarie University offers a range of Student Support Services including:
Got a question? Ask us via AskMQ, or contact Service Connect.
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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 2022.04 of the Handbook