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 6.4.2006

Lecture notes

• Chapter 1, Multiple Linear Regression
• Chapter 2, Nonparametric Density Estimation
• Chapter 3, Nonparametric Regression
• Chapter 4, Cross-Validation
• Chapter 5, Bootstrap [notes are in file for 'Ch. 4']
• Chapter 6, Classification
• Chapter 7, Flexible regression and classification methods (in parts 1, 2, 3; 4)
          + Lasso extended
• Chapter 8, Bagging and Boosting         (2x1 up slides of talk on Boosting, Lasso, ...)

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 and required for credits to non-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