Skip to content

Bootstrap Techniques for Signal Processing

Spend $50 to get a free DVD!

ISBN-10: 0521034051

ISBN-13: 9780521034050

Edition: N/A

Authors: Abdelhak M. Zoubir, D. Robert Iskander

List price: $57.99
Shipping box This item qualifies for FREE shipping.
Blue ribbon 30 day, 100% satisfaction guarantee!
what's this?
Rush Rewards U
Members Receive:
Carrot Coin icon
XP icon
You have reached 400 XP and carrot coins. That is the daily max!


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…    
Customers also bought

Book details

List price: $57.99
Publisher: Cambridge University Press
Publication date: 2/15/2007
Binding: Paperback
Pages: 232
Size: 6.69" wide x 9.61" long x 0.47" tall
Weight: 0.836
Language: English

Abdelhak M. Zoubir received the Dipl.-Ing degree (BSc/BEng) from Fachhochschule Niederrhein, Germany, in 1983, the Dipl.-Ing. (MSc/MEng) and the Dr.-Ing. (PhD) degree from Ruhr University Bochum, Germany, in 1987 and 1992, all in Electrical Engineering. Early placement in industry (Kl�ckner-Moeller and Siempelkamp AG) was then followed by Associate Lectureship in the Division for Signal Theory at Ruhr University Bochum, Germany. In June 1992, he joined Queensland University of Technology where he was Lecturer, Senior Lecturer and then Associate Professor in the School of Electrical and Electronic Systems Engineering. In March 1999, he took up the position of Professor of…    

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 fields such as statistical signal processing, visual optics, and ophthalmic instrumentation.

The bootstrap principle
The principle of resampling
Some theoretical results for the mean
Examples of non-parametric bootstrap estimation
The parametric bootstrap
Bootstrap resampling for dependent data
Examples of dependent data bootstrap estimation
The principle of pivoting and variance stabilisation
Some examples
Limitations of the bootstrap
Trends in bootstrap resampling
Signal detection with the bootstrap
Principles of hypothesis testing
Sub-optimal detection
Hypothesis testing with the bootstrap
The role of pivoting
Variance estimation
Detection through regression
The bootstrap matched filter
Tolerance interval bootstrap matched filter
Bootstrap model selection
Model selection
Model selection in linear models
Model selection based on prediction
Bootstrap based model selection
A consistent bootstrap method
Dependent data in linear models
Model selection in nonlinear models
Data model
Use of bootstrap in model selection
Order selection in autoregressions
Detection of sources using bootstrap techniques
Bootstrap based detection
Null distribution estimation
Bias correction
Real data bootstrap applications
Optimal sensor placement for knock detection
Data model
Bootstrap tests
The experiment
Confidence intervals for aircraft parameters
Results with real passive acoustic data
Landmine detection
Noise floor estimation in over-the-horizon radar
Principle of the trimmed mean
Optimal trimming
Noise floor estimation
Model order selection for corneal elevation
Matlab codes for the examples
Basic non-parametric bootstrap estimation
The parametric bootstrap
Bootstrap resampling for dependent data
The principle of pivoting and variance stabilisation
Limitations of bootstrap procedure
Hypothesis testing
The bootstrap matched filter
Bootstrap model selection
Noise floor estimation
Bootstrap Matlab Toolbox