Bootstrap Techniques for Signal Processing by Abdelhak M. ZoubirBootstrap Techniques for Signal Processing by Abdelhak M. Zoubir

Bootstrap Techniques for Signal Processing

byAbdelhak M. Zoubir, D. Robert Iskander

Paperback | February 26, 2007

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The statistical bootstrap is one of the methods that can be used to calculate estimates of a certain number of unknown parameters of a random process or a signal observed in noise, based on a random sample. Such situations are common in signal processing and the bootstrap is especially useful when only a small sample is available or an analytical analysis is too cumbersome or even impossible. This book covers the foundations of the bootstrap, its properties, its strengths, and its limitations. The authors focus on bootstrap signal detection in Gaussian and non-Gaussian interference as well as bootstrap model selection. The theory developed in the book is supported by a number of practical examples written in MATLAB. The book is aimed at graduate students and engineers, and includes applications to real-world problems in areas such as radar and sonar, biomedical engineering, and automotive engineering.
D. Robert Iskander received his Ph.D. from Queensland University of Technology. He is currently a senior lecturer in the School of Engineering at Griffith University, Australia. He is also a visiting research fellow in the Centre for Eye Research at Queensland University of Technology. He has published over 50 technical papers, in fiel...
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Title:Bootstrap Techniques for Signal ProcessingFormat:PaperbackDimensions:232 pages, 9.72 × 6.85 × 0.47 inPublished:February 26, 2007Publisher:Cambridge University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0521034051

ISBN - 13:9780521034050

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Table of Contents

Preface; Notations; 1. Introduction; 2. The bootstrap principle; 3. Signal detection with the bootstrap; 4. Bootstrap model selection; 5. Real data bootstrap applications; Appendix 1. MATLAB codes for the examples; Apendix 2. Bootstrap MATLAB toolbox; References; Index.

Editorial Reviews

"...well written and an interesting read with some good examples. The Matlab code supplied will no doubt be appreciated...and the algorithms were well displayed, allowing a programmer to easily implement them in the very useful addition."
Angelo J. Canty, McMaster University for the Journal of the American Statistical Association