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STATS 730: Semester 2, 2011
Statistical inference based on the likelihood,
with examples in R, SAS and ADMB

Taught by:

Dr Russell Millar

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 homeworks.) 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. It also gives brief exposure to ADMB - the most powerful optimization software available today.