Subject description - XP33RSK
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Explanatory Notes
Instructions
| XP33RSK | Robust Statistics for Cybernetics | ||
|---|---|---|---|
| Roles: | S | Extent of teaching: | 2P+0S |
| Department: | 13133 | Language of teaching: | CS |
| Guarantors: | Nosková J. | Completion: | ZK |
| Lecturers: | Nosková J. | Credits: | 4 |
| Tutors: | Nosková J. | Semester: | L |
Anotation:
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).Course outlines:
Exercises outline:
Literature:
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 YorkRequirements:
Subject is included into these academic programs:| Program | Branch | Role | Recommended semester |
| DOKP | Common courses | S | – |
| DOKK | Common courses | S | – |
| Page updated 1.3.2021 17:52:22, semester: Z/2020-1, L/2021-2, L/2020-1, Z/2021-2, Send comments about the content to the Administrators of the Academic Programs | Proposal and Realization: I. Halaška (K336), J. Novák (K336) |