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.

Start of lectures

Thursday, 22.3.2007 (Exercises start at 23.3. with a special introduction to the "R" software)

Lecture notes                                                   (print yourself!)

• Chapter 1, Multiple Linear Regression --- lecture notes for chapters 1-4 ---
• 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; RandomForest
• Chapter 8, Bagging and Boosting ; + 2x1 up slides of talk on 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 strongly recommended for all and required for credits for all students (with possible and rare exceptions for PhD-graduate students).

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