Robust Statistics for Signal Processing
$145.00 (C)
- Authors:
- Abdelhak M. Zoubir, Technische Universität, Darmstadt, Germany
- Visa Koivunen, Aalto University, Finland
- Esa Ollila, Aalto University, Finland
- Michael Muma, Technische Universität, Darmstadt, Germany
- Date Published: December 2018
- availability: Available
- format: Hardback
- isbn: 9781107017412
$
145.00
(C)
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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.
Read more- 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
- Date Published: December 2018
- format: Hardback
- isbn: 9781107017412
- length: 312 pages
- dimensions: 253 x 178 x 18 mm
- weight: 0.77kg
- availability: Available
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.-
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Robust Statistics for Signal Processing
Abdelhak M. Zoubir, Visa Koivunen, Esa Ollila, Michael Muma
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