STATS 730 Statistical Inference

Below description edited in year: 2008

Points: 15

Prereqs: STATS 310

Credit: Internal assessment 40%, final exam 60%

Textbooks: None prescribed, but a reading list will be given out.

For Advice: Russell Millar (Email: r.millar@auckland.ac.nz | extn: 85003)

Taught: First Semester City

Website: STATS 730 website

STATS 730 gives you general-purpose skills to model real data, using likelihood-based statistical inference. It begins by looking at how likelihood is used by frequentist and Bayesian inference, and uses statistical brain-teasers to demonstrate the difference between these two paradigms. Focus then shifts to establishing the properties of maximum likelihood inference within the frequentist paradigm. Maximum likelihood is then applied in a wide variety of settings with examples in both R and SAS. (Students may chose either of these languages for their homework.) The course concludes by looking at extensions of maximum likelihood for models with nuisance parameters, including quasi-likelihood, conditional likelihood, and mixture models.

STATS 730 provides the tools and skills used by many other graduate courses on offer in this department, and it gives exposure to statistical programming in both R and SAS.

To understand the theory it is necessary that students are concurrently enrolled in (or have already completed) STATS 310. Students are also expected to have a good grasp of second-year calculus: multiple integrals, partial derivatives and Taylor series expansions will be used in this course.

Disclaimer:
Although every reasonable effort is made to ensure accuracy, this information for the course year (2008), is provided as a general guide only for students and is subject to alteration. All students enrolling at the University of Auckland must consult its official document, the University of Auckland Calendar, to ensure that they are aware of and comply with all regulations, requirements and policies.