000			02189nam a22003618i 4500
001			CR9780511536717
003			UkCbUP
005			20200124160323.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			090430s2004||||enk o ||1 0|eng|d
020			_a9780511536717 (ebook)
020			_z9780521831277 (hardback)
020			_z9780521034050 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aTK5102.9 _b.Z68 2004
082	0	0	_a621.382/2 _222
100	1		_aZoubir, Abdelhak M., _eauthor.
245	1	0	_aBootstrap techniques for signal processing / _cAbdelhak M. Zoubir, D. Robert Iskander.
264		1	_aCambridge : _bCambridge University Press, _c2004.
300			_a1 online resource (xiv, 217 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
520			_aThe 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.
650		0	_aSignal processing _xMathematics.
650		0	_aImage processing _xMathematics.
650		0	_aBootstrap (Statistics)
700	1		_aIskander, D. Robert, _eauthor.
776	0	8	_iPrint version: _z9780521831277
856	4	0	_uhttps://doi.org/10.1017/CBO9780511536717
999			_c522010 _d522008