Subject description - XP33RSK

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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).

Content:

1. Classical and robust statistics
2. An estimation of location and scale
3. The maximum likelihood estimation
4. M-estimators of location
5. Influence function
6. Breakdown point
7. M-estimators of scale
8. Asymptotic normality of M-estimators
9. Balancing bias and variance
10. Hampel‘s optimality
11. Linear model and LS method
12. M-estimators in linear model
13. Linear model with random predictors
14. S-estimators(LTS) in linear model

Course outlines:

Exercises outline:

Literature:

Ricardo A. Maronna, R. Douglas Martin, Victor J. Yohai, Matías Salibián-Barrera, Robust Statistics: Theory and Methods (with R), 2nd Edition ISBN: 978-1-119-21466-3 October 2018 464 Pages 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

Requirements:

Subject is included into these academic programs:

Program Branch Role Recommended semester
DOKP Common courses S
DOKK Common courses S


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