Computational Statistics
Spring semester 2020
General information
| Lecturer | Marloes Maathuis |
|---|---|
| Assistants | Drago Plecko, Corinne Emmenegger, Meta-Lina Spohn |
| Lectures | Thu 13-15 (via Zoom, see Moodle)
Fri 09-10 (via Zoom, see Moodle) |
| Exercises | Fri 10-11 (via Zoom, see Moodle . Please stay in the zoom call of the exercise hour if you have questions.) |
| Course catalogue data | >> |
| Credit points | In order to obtain ECTS credit points, you need to pass the official exam. In order to obtain ETH credit points (this only applies to doctoral students from certain departments), you need to solve and submit at least 8 exercise series. |
Course content
We will study modern statistical methods for data analysis, including their algorithmic aspects and theoretical properties. Special emphasis will be placed on re-sampling based methods for inference. The course is hands-on, and methods are applied using the statistical programming language R.
The exam will cover all material that is discussed during the lectures and the exercise sessions. If you miss a class, please make sure to copy class notes from someone else.
Material and Recordings
The material for each week consisting of slides, series, solution, R-code and video-recordings can be found on Moodle. On Moodle you also find a Literature section for for the main books that we use. We recommend to attend the lectures and exercise classes live via Zoom. Please refer to Moodle to obtain the links.All discussions and announcements regarding the course can be found in the Moodle forum.
Course material
Please refer to Moodle for all the course material.Exercise classes
Please refer to Moodle for all the exercise material. The new exercise sheet will be uploaded on the Thursday preceding the pre-discussion.The exercises form a very important part of the course. They are also important for exam preparation, since a considerable part of the exam requires the use of R. During the exercise class, the assistants will discuss the last series and introduce the new series, often showing some example code as well.
Unless you are a PhD student who requires ETH credit points, you do not have to hand in your exercises. We will provide solutions, and you are expected to check your own work. Please ask if anything is unclear, for example if you don't understand the solutions, or if you found a different solution.
PhD students who require ETH credit points should email the solved exercises to compstat@stat.math.ethz.ch by 10am on the Friday following the pre-discussion. You need to hand in at least 8 exercise series to obtain the ETH credit points.
Literature
Main literature
- P. Bühlmann, M. Mächler. Script Computational Statistics. (Version of October 12, 2016).
- G. James, D. Witten, T. Hastie, R. Tibshirani. An Introduction to Statistical Learning: with Applications in R [ISLR]. Springer.
- T. Hastie, R. Tibshirani, J. Friedman. The Elements of Statistical Learning [ESL]. Springer.
Other relevant literature
- C. R. Shalizi. Advanced Data Analysis from an Elementary Point of View.
- B. Efron, T. Hastie. Computer Age Statistical Inference. Cambridge University Press.
- J. E. Gentle. Elements of Computational Statistics. Springer.
- W. N. Venables, B. D. Ripley. Modern Applied Statistics with S. Springer.
- L. Wasserman. All of statistics. Springer.