Computational Statistics

Course Synopsis

Multiple regression, nonparametric methods for regression and classification (kernel estimates, smoothing splines, regression and classification trees, additive models, projection pursuit, neural nets), curse of dimensionality, resampling, bootstrap, cross validation. 

(more details: Course Catalogue Data)

Start of lectures

Thursday, 21.02.2013 (Exercises start on 22.02. with a special introduction to the "R" software)

Lecture notes                       (PDF print yourself!)

• Chapter 1, Multiple Linear Regression
• Chapter 2, Nonparametric Density Estimation
• Chapter 3, Nonparametric Regression
• Chapter 4, Cross-Validation
• Chapter 5, Bootstrap
• Chapter 6, Classification
• Chapter 7, Flexible regression and classification methods + 1 page on "Random Forests"
• Chapter 8, Bagging and Boosting + Slides about 'Boosting'

R Scripts as used in the Lecture

A selection is online in this ftp  directory.

Exercises

Exercises will be based on the free open-source statistics and graphics software R. Emphasis will be put on applied problems. Active participation in the exercises is recommended.

Contacting lecturers and assistants

Of course you can always ask questions during the class and the exercise sessions. If you want to contact us by email, please write to compstat@stat.math.ethz.ch, NOT directly to a assistant.

Lecture attestation (Testat):

In order to obtain ETCS-credits you have to pass the written exam during the examination session.

If you need ETH-credits (for PhD students from D-MATH and D-ITET) you need to hand in your exercises and achieve a minimal amount of 40 points. (see the exercise page for more details)

Recommended Reading

T. Hastie, R. Tibshirani, J. Friedman. The Elements of Statistical Learning. Springer

J. E. Gentle. Elements of Computational Statistics. Springer

W. N. Venables, B. D. Ripley. Modern Applied Statistics with S. Springer