Robust statistics for signal processing
著者
書誌事項
Robust statistics for signal processing
Cambridge University Press, 2018
- : hardback
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注記
Other authors: Visa Koivunen, Esa Ollila, Michael Muma
Bibliography: p. 272-287
Includes index
内容説明・目次
内容説明
Understand the benefits of robust statistics for signal processing with this authoritative yet accessible text. The first ever book on the subject, it provides a comprehensive overview of the field, moving from fundamental theory through to important new results and recent advances. Topics covered include advanced robust methods for complex-valued data, robust covariance estimation, penalized regression models, dependent data, robust bootstrap, and tensors. Robustness issues are illustrated throughout using real-world examples and key algorithms are included in a MATLAB Robust Signal Processing Toolbox accompanying the book online, allowing the methods discussed to be easily applied and adapted to multiple practical situations. This unique resource provides a powerful tool for researchers and practitioners working in the field of signal processing.
目次
- 1. Introduction and foundations
- 2. Robust estimation: the linear regression model
- 3. Robust penalized regression in the linear model
- 4. Robust estimation of location and scatter (covariance) matrix
- 5. Robustness in sensor array processing
- 6. Tensor models and robust statistics
- 7. Robust filtering
- 8. Robust methods for dependent data
- 9. Robust spectral estimation
- 10. Robust bootstrap methods
- 11. Real-life applications.
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