Seminar for Statistics

Computational Statistics

Dr. Martin Maechler, Prof. Peter Bühlmann Lectures: Th 13-15, HG F3, Fr   9-10, HG D7.1
Tutors: Bernadetta Tarigan (main),
Nicoleta Gosoniu
Exercises: Fr 10-12, HG D7.1

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

R Scripts as used in the Lecture

A selection is online in this ftp  directory.


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


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