|
|
|
||
Last update: doc. Ing. Marek Omelka, Ph.D. (30.11.2020)
|
|
||
Last update: doc. Ing. Marek Omelka, Ph.D. (14.02.2023)
To understand principles of robust methods.
|
|
||
Last update: RNDr. Jitka Zichová, Dr. (03.06.2022)
Written and oral exam. |
|
||
Last update: Mgr. Stanislav Nagy, Ph.D. (07.11.2023)
Huber, P. J.; Ronchetti, E. M. (2009). Robust statistics. Second edition. Wiley Series in Probability and Statistics. John Wiley & Sons, Inc., Hoboken, NJ. xvi+354 pp.
Jurečková, J. (2001). Robustní statistické metody. Karolinum.
Maronna, R. A.; Martin, R. D.; Yohai, V. J. (2006). Robust statistics: Theory and methods. Wiley Series in Probability and Statistic. John Wiley & Sons, Ltd., Chichester, xx+436 pp.
|
|
||
Last update: doc. Ing. Marek Omelka, Ph.D. (03.12.2020)
Lecture. |
|
||
Last update: doc. Ing. Marek Omelka, Ph.D. (14.02.2023)
The requirements for the oral exam are in agreement with the syllabus of the course as presented during lectures. |
|
||
Last update: Mgr. Stanislav Nagy, Ph.D. (07.11.2023)
1. Classical and robust statistics - overview and main principles
2. Theoretical basics: the space of measures and its topology, functional derivatives
3. Statistical functional and its estimator, influence function, breakdown point
4. Basic types of estimators: M-estimators, Z-estimators, L-estimators, R-estimators
5. Minimax optimality of robust estimators of location
6. Further topics: Robust estimation of scale, robustness in regression, estimation for multidimensional data. Computational aspects.
|
|
||
Last update: Mgr. Stanislav Nagy, Ph.D. (07.11.2023)
Basic knowledge of mathematical analysis, probability theory and mathematical analysis. |