STAT380 Statistical Computing
This course develops a working knowledge of several topics in modern statistical computing. We focus on a practical understanding of how and why such methods work rather than their analytic bases. A second aim is to learn a suitable language that allows computer implementation of the statistical ideas covered.
This paper is intended for students with some exposure to statistical techniques.
STAT 241 + 18 additional points of 200-level statistics
- Introduction to R
- Bootstrap and randomization procedures
- Optimization, numerical integration and maximum likelihood estimation
- Simulation and MCMC methods
There is no prescribed textbook, but some texts that may be useful include:
- Peter Dalgaard, Introductory Statistics with R, Springer, 2002. ISBN 0-387-95475-9.
- Anthony Davison and D.V. Hinkley, Bootstrap Methods and their Application, Cambridge University Press, 1997.
- Geof H. Givens and Jennifer A. Hoeting, Computational Statistics, Wiley, 2005. ISBN 0-471-46124-5.
- William N. Venables and Brian D. Ripley, Modern Applied Statistics with S, Fourth Edition, Springer, New York, 2002. ISBN 0-387-95457-0.
Monday and Wednesday at 9:00 - 9:50 AM; room B21. These are compulsory.
Tuesday at 9:00 - 9:50 AM; room B21. This is compulsory.
The internal assessment will make up 40% of the final mark. Six assignments will make up a total of 25% of the final mark. A mid-term test will make up 15% of the final mark.
The final exam will be worth 60% of the final mark. A minimum score of 40% is required on the exam in order to pass the paper.