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Robust statistics for signal processing / Abdelhak M. Zoubir, Visa Koivunen, Esa Ollila, Michael Muma.

By: Zoubir, Abdelhak M., author.
Contributor(s): Koivunen, Visa, author. | Ollila, Esa,, 1974- author. | Muma, Michael,, 1981- author.
Call number: QA276 Z68 2018eb Material type: TextTextPublisher: Cambridge : Cambridge University Press, 2018Description: 1 online resource (xxii, 291 pages) : digital, PDF file(s).Content type: text Media type: computer Carrier type: online resourceISBN: 9781139084291 (ebook) ; 9781107017412 (hardback) (Invalid ISBN) Subject(s): Robust statistics | Signal processing -- MathematicsAdditional physical formats: Print version: : No titleDDC classification: 519.5 Online resources: Click here to access online Summary: 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.
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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.

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