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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2023/2024
UPOZORNĚNÍ: Jsou dostupné studijní plány pro následující akademický rok.

Robust Statistics for Cybernetics

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Code Completion Credits Range Language
XP33RSK ZK 4 2P+0S Czech
Garant předmětu:
Jana Nosková
Lecturer:
Jana Nosková
Tutor:
Jana Nosková
Supervisor:
Department of Cybernetics
Synopsis:

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).

Requirements:
Syllabus of lectures:
Syllabus of tutorials:
Study Objective:
Study materials:

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

Note:
Time-table for winter semester 2023/2024:
Time-table is not available yet
Time-table for summer semester 2023/2024:
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue
Wed
roomKN:G-205
Nosková J.
13:30–15:15
(lecture parallel1)
Karlovo nám.
seminární místnost
Thu
Fri
The course is a part of the following study plans:
Data valid to 2024-05-28
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