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Course Catalog 2012-2013
SGN-2756 Robust Estimation, 3 cr |
Additional information
Contact the person in charge if you want to take the course. There will be no lectures. Exam material will be independently studied by the student.
Suitable for postgraduate studies
Person responsible
Sari Peltonen
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Requirements
Examination and an individual assignment.
Completion parts must belong to the same implementation
Principles and baselines related to teaching and learning
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Learning outcomes
After completing the course, the student - is able to explain the meaning of robustness, deviations from parametric models and estimation theory, - can apply tools (influence function (IF), gross-error sensitivity, local-shift sensitivity, rejection point, asymptotic variance, breakdown point) for assessing the robustness of estimators, - is able to design basic robust estimators, and - is able to do a small assignment on a robustness related topic.
Content
| 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. |
Evaluation criteria for the course
Grading is pass/fail. In order to pass the student has to do an individual assingment and get at least half of the maximum points from the final exam.
Assessment scale:
Evaluation scale passed/failed will be used on the course
Partial passing:
Study material
| Type | Name | Author | ISBN | URL | Edition, availability, ... | Examination material | Language |
| Book | Robust Statistics | Hampel, Ronchetti, Rousseeuw & Stahel | Two first chapters of the book are exam material. | 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 |
Prerequisites
| Course | Mandatory/Advisable | Description |
| SGN-2607 Statistical Signal Processing | Advisable | |
| SGN-2706 Nonlinear Signal Processing | Advisable |
Prerequisite relations (Requires logging in to POP)
Correspondence of content
| Course | Corresponds course | Description |
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More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |
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Contact teaching: 0 % Distance learning: 0 % Self-directed learning: 0 % |