- Code STAT8027
- 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 Statistics
- Academic career PGRD
- Dr Anton Westveld
- Mode of delivery In Person
First Semester 2015
See Future Offerings
This course introduces students to the basic theory behind the development and assessment of statistical analysis techniques in the areas of point and interval estimation and hypothesis testing.
Topics include: Point estimation methods, including method of moments and maximum likelihood; Bias and variance; Mean-squared error and the Cramer-Rao inequality; Sufficiency, completeness and exponential families; the Rao-Blackwell theorem and uniformly minimum variance unbiased estimators; Bayesian estimation methods; Resampling estimation methods, including the jackknife and the bootstrap; Confidence interval construction methods, including likelihood-based intervals, inversion methods, intervals based on pivots and simple resampling-based percentile intervals; Highest posterior density and Bayesian credibility regions; Likelihood ratio tests and the Neymann-Pearson lemma; Power calculations and uniformly most powerful tests; Rank-based non-parametric tests, including the sign-test and Wilcoxon tests.
Upon successful completion, students will have the knowledge and skills to:
Upon successful completion of the requirements of this course, students should have the knowledge and skills to:
- explain in detail the notion of a parametric model and point estimation of the parameters of those models.
- explain in detail and demonstrate approaches to include a measure of accuracy for estimation procedures and our confidence in them by examining the area of interval estimation.
- demonstrate the plausibility of pre-specified ideas about the parameters of the model by examining the area of hypothesis testing.
- explain in detail and demonstrate the use of non-parametric statistical methods, wherein estimation and analysis techniques are developed that are not heavily dependent on the specifications of an underlying parametric model.
- demonstrate computational skills to implement various statistical inferential approaches.
See the course outline on the College courses page. Outlines are uploaded as they become available.
- Tutorial Question - 5%
- Presentation/Project - 15%
- Mid-Semester Exam - 20% or 0%, redeemable in favour of the final
- Compulsory Final Exam - 60% or 80%
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10 hours per week.
Requisite and Incompatibility
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- Student Contribution Band:
- Unit value:
- 6 units
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Offerings, Dates and Class Summary Links
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Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
|Class number||Class start date||Last day to enrol||Census date||Class end date||Mode Of Delivery||Class Summary|
|2682||16 Feb 2015||06 Mar 2015||31 Mar 2015||29 May 2015||In Person||N/A|