STAT 8670 - Computational Methods in Statistics
Preface
Description
Topics included are optimization, numerical integration, bootstrapping, cross-validation and Jackknife, density estimation, smoothing, and use of the statistical computer package of S-plus/R.
Prerequisites
MATH 4752/6752 – Mathematical Statistics II, and the ability to program in a high-level language.
Instructor
Chi-Kuang Yeh, I am a postdoctoral scholar at the Department of Statistics and Actuarial Science, McGill University.
- Office: 1216 Burnside Hall.
- Email: chi-kuang.yeh@mail.mcgill.ca.
Office Hour
[By appointment and a online link will be provided later]
Assignment
Midterm
Topics and Corresponding Lectures
Those chapters are based on the lecture notes. This part will be updated frequently.
| Topic | Lecture Covered |
|---|---|
| Optimization | TBA |
| Numerical integration | TBA |
| Jackknife | TBA |
| Bootstrap | TBA |
| Cross-validation | TBA |
| Smoothing | TBA |
| Density estimation | TBA |
| Monte Carlo Methods | TBA |
Recommended Textbooks
Givens, G.H. and Hoeting, J.A. (2012). Computational Statistics. Wiley, New York.
Rizzo, M.L. (2007) Statistical Computing with R. CRC Press, Roca Baton.
Hothorn, T. and Everitt, B.S. (2006). A Handbook of Statistical Analyses Using R. CRC Press, Boca Raton.
Side Readings
- Wickham, H., Çetinkaya-Rundel, M. and Grolemund, G. (2023). R for Data Science. O’Reilly.