Robust Statistics for Cybernetics
- Department of Cybernetics
Statistical methods are basic tools of control and decision making theory. Classical statistical methods (e.g. MLE) are usually very sensitive to deviations from our idealized model. Thus many methods which are robust have been developed. It means that these methods are not so sensitive to small deviations from an underlying model. So we briefly explain the parametric concept of estimation and then we introduce the robust approach, some basic robust estimators of location (e.g. trimmed mean, Hampel estimator) and measures of robustness (influence function, breakdown point).
- Syllabus of lectures:
- Syllabus of tutorials:
- Study Objective:
- Study materials:
Rousseeuw,P.J., Leroy,A. (1987) Robust Regression and Outlier Detection.
Wiley, New York
Huber,P.J. (1981) Robust Statistics.Wiley,New York
Hampel,F.R.,Ronchetti, E.M.,Rousseeuw, P.J.,Stahel,W.A. (1986) Robust
Statistics: The Approach Based on Influence Functions. Wiley,New York
Dodge,Y., Jureckova,J. (2000) Adaptive Regression. Springer, New York
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans: