STAT 8670 - Computational Methods in Statistics
Preface
Description
Topics ins 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 an Assistant Professor in the Department of Mathematics and Statistics, Georgia State University.
- Office: Suite 1407, 25 Park Place.
- Email: cyeh@gsu.edu.
Office Hour
14:00–15:00 on Tuesday and Wednesday.
Grade Distribution
- Assignments: 40%
- Term Exam 1: 15%
- Term Exam 2: 15%
- Final Project: 30%
Assignment
Midterm
Final Project
Topics and Corresponding Lectures
Those chapters are based on the lecture notes. This part will be updated frequently.
| Topic | Lecture Covered |
|---|---|
| Introduction to R Programming | 1–2 |
| Numerical Approaches and Optimization in 1-D | 3–5 |
| Review for Distribution | 6–7 |
| Random Variable Generation | 8–10 |
| Monte Carlo and Integration | 11– |
| Term Exam 1 | 13 |
| 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.