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Robust Statistics for Signal Processing

$145.00 (C)

  • Publication planned for: December 2018
  • availability: Not yet published - available from December 2018
  • format: Hardback
  • isbn: 9781107017412

$ 145.00 (C)
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About the Authors
  • 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.

    • The first ever book on robust signal processing
    • Covers important new results and recent developments in robust signal processing
    • Includes real-world examples from the authors' experience, demonstrating the relevance of the methods discussed
    • Includes the key algorithms in a MATLAB Robust Signal Processing Toolbox, allowing methods to be easily applied
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    Product details

    • Publication planned for: December 2018
    • format: Hardback
    • isbn: 9781107017412
    • length: 312 pages
    • dimensions: 253 x 178 x 18 mm
    • weight: 0.77kg
    • availability: Not yet published - available from December 2018
  • Table of Contents

    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.

  • Resources for

    Robust Statistics for Signal Processing

    Abdelhak M. Zoubir, Visa Koivunen, Esa Ollila, Michael Muma

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    Here you will find free-of-charge online materials to accompany this book. The range of materials we provide across our academic and higher education titles are an integral part of the book package whether you are a student, instructor, researcher or professional.

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  • Authors

    Abdelhak M. Zoubir, Technische Universität, Darmstadt, Germany
    Abdelhak M. Zoubir is a Professor of Signal Processing and the Head of the Signal Processing Group at Technische Universität, Darmstadt, Germany. He is a Fellow of the IEEE, an IEEE Distinguished Lecturer, and the co-author of Bootstrap Techniques for Signal Processing (Cambridge, 2004).

    Visa Koivunen, Aalto University, Finland
    Visa Koivunen is a Professor of Signal Processing at Aalto University, Finland. He is also a Fellow of the IEEE and an IEEE Distinguished Lecturer.

    Esa Ollila, Aalto University, Finland
    Esa Ollila is an Associate Professor of Signal Processing at Aalto University, Finland.

    Michael Muma, Technische Universität, Darmstadt, Germany
    Michael Muma is a Postdoctoral Research Fellow in the Signal Processing Group at Technische Universität, Darmstadt, Germany.

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