國立虎尾科技大學 |

Bootstrap techniques for signal processing /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Bootstrap techniques for signal processing // Abdelhak M. Zoubir, D. Robert Iskander.
Author:	Zoubir, Abdelhak M.,
other author:	Iskander, D. Robert,
Description:	1 online resource (xiv, 217 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
Subject:	Signal processing - Mathematics. -
Online resource:	https://doi.org/10.1017/CBO9780511536717
ISBN:	9780511536717 (ebook)

Bootstrap techniques for signal processing /
Zoubir, Abdelhak M.,

Bootstrap techniques for signal processing /Abdelhak M. Zoubir, D. Robert Iskander. - 1 online resource (xiv, 217 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

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 useful 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.

ISBN: 9780511536717 (ebook)Subjects--Topical Terms:

528164
Signal processing
--Mathematics.

LC Class. No.: TK5102.9 / .Z68 2004

Dewey Class. No.: 621.382/2

Bootstrap techniques for signal processing /
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520 $a 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 useful 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.
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