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SGN-2756 Robust Estimation, 3 cr |
Sari Peltonen
No implementations
One seminar presentation and final exam.
Completion parts must belong to the same implementation
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After passing this course the student understands what robustness means and knows which tools can be used for studying robustness of estimators (filters). Student will also become familiar with several robust estimators.
Content | Core content | Complementary knowledge | Specialist knowledge |
1. | The meaning of robustness, deviations from parametric models and estimation theory. | Chebychev's inequality, information inequality, unbiasedness, consistency, efficiency and basics of detection. | Minimax approach. |
2. | Influence function (IF), gross-error sensitivity, local-shift sensitivity, rejection point, asymptotic variance, breakdown point. | Finite-sample versions of the IF and output distributional influence function (ODIF). | |
3. | Order Statistics (OS) and stack filters and their distributions. | Optimization of the stack filters. | |
4. | Definitions of M-, L-, and R-estimators. | Specific properties of these different estimator classes. Redescending M-estimators and matched median filter. |
Grading is pass/fail. In order to pass the student has to give seminar presentation and get at least half of the maximum points from the final exam.
Evaluation scale passed/failed will be used on the course
Type | Name | Author | ISBN | URL | Edition, availability, ... | Examination material | Language |
Book | Robust Statistics | Hampel, Ronchetti, Rousseeuw & Stahel | John Wiley, 1985 | English | |||
Book | Robust Statistics | Huber | John Wiley, 1981 | English | |||
Book | Robust Statistics | Maronna Ricardo, Martin Doug and Yohai Victor | 978-0-470-01092-1 | John Wiley, 2006 | English | ||
Lecture slides | "Robust Estimation" | Sari Peltonen | English |
Course | O/R |
SGN-2706 Nonlinear Signal Processing | Recommended |
Course | Corresponds course | Description |
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Next implementation during academic year 2009-2010.