STATS 730: MOVED TO CANVAS Semester 2, 2016
Statistical inference based on the likelihood,
with examples in R, SAS and ADMB

Taught by:

Assoc Prof Russell Millar

STATS 730 gives you general-purpose skills to model real data, using likelihood-based statistical inference under the frequentist paradigm. It begins with a gentle introduction to maximum likelihood and the notation that will be used throughout, followed by simple and not-so-simple (e.g., finite mixture model) iid examples. The essential properties and tools of maximum-likelihood inference are then presented. Maximum likelihood is then applied in a wide variety of settings with examples in both R and SAS, and ADMB where needed. (Students may choose any of these languages for their homeworks.) After covering the theory and practice of generalized linear models the course concludes by looking at mixture models, including nonlinear and generalized linear mixed models.

STATS 730 provides the tools and skills used by many other graduate courses on offer in this department, and of invaluable use to students undertaking MSc projects or beginning PhD study. It gives strong exposure to statistical programming in both R and SAS, and brief exposure to ADMB - the most powerful optimization software available today.