STATS 732 Topics in Statistical Inference

Below description edited in year: 2008

Points: 15

Credit: Examination 60%, assignments and test 40%

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

For Advice: Chris Triggs (Email: triggs@stat.auckland.ac.nz | extn: 85556)

Taught: First Semester City

Website: STATS 732 website

This course includes the material in STATS 310.
It is intended for postgraduate students who have not already passed STATS 310.

Students will attend lectures and tutorials for STATS 310. Additional material on Decision Theory and Bayesian Inference will be covered in STATS 732 workshops and tutorials.

Topics studied include:

Maximum likelihood estimation, likelihood and score functions, maximum likelihood estimates, Cramér-Rao lower bound, asymptotic optimality of maximum likelihood estimates, construction of confidence intervals. Multivariate Distributions, joint, marginal, and conditional distributions, vector random variables, variances and covariances, conditional means and variances, maximum likelihood estimates for multivariate parameters. Hypothesis testing, power and size of hypothesis tests, Neyman-Pearson lemma, link between hypothesis tests and confidence intervals. General linear models, least squares estimates, theory of estimation and testing for linear models Decision theory and Bayesian inference, decision rules, loss functions, risk functions, minimax rules, Bayes rules.

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.