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 31.3.2005

Lecture notes

• 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
  Section 7.7, Ridging and the Lasso
• Chapter 8, Bagging and Boosting

Exercises

Exercises will be based on the open-source statistics software R. Emphasis will be put on applied problems. Active participation in the exercises is strongly recommended. You can find the series & solutions and more information here.

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