- Code STAT3036
- Unit Value 6 units
- Offered by Rsch Sch of Finance, Actuarial Studies & App Stats
- ANU College ANU College of Business and Economics
- Course subject Statistics
- Areas of interest Actuarial Studies, Statistics
- Academic career UGRD
- Dr Borek Puza
- Mode of delivery In Person
Second Semester 2019
See Future Offerings
This course covers the fundamental concepts of: Bayesian statistics, including estimation, prediction, hypothesis testing, and decision theory; time series analysis, including estimation and prediction based on ARIMA models; credibility theory, including limited fluctuation credibility theory and the Buhlmann-Straub model; several run-off techniques for estimating an outstanding claims reserve; and Monte Carlo techniques, including the inverse transformation method, the polar method, and Monte Carlo integration.
Upon successful completion, students will have the knowledge and skills to:
- Explain the fundamental concepts of Bayesian statistics and use these concepts to calculate Bayesian estimators (including credibility estimators).
- Define and apply the main concepts underlying the analysis of time series models.
- Describe and apply techniques for analysing a delay (or run-off) triangle and projecting the ultimate position.
- Explain and apply the concepts of “Monte Carloâ€? simulation using a series of pseudo-random numbers.
See the course outline on the College courses page. Outlines are uploaded as they become available.
- Typical assessment may include, but is not restricted to: a mid-semester exam and a final exam (null) [LO null]
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Students are expected to commit at least 10 hours per week to completing the work in this course. This will include at least 3 contact hours per week and up to 7 hours of private study time.
Requisite and Incompatibility
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- Student Contribution Band:
- Unit value:
- 6 units
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