LEADER 03459nam 2200733Ia 450 001 9910818335603321 005 20200520144314.0 010 $a1-107-14842-1 010 $a1-280-47787-3 010 $a9786610477876 010 $a0-511-19529-X 010 $a0-511-19595-8 010 $a0-511-19389-0 010 $a0-511-33144-4 010 $a0-511-53671-2 010 $a0-511-19463-3 035 $a(CKB)1000000000353231 035 $a(EBL)259893 035 $a(OCoLC)173610030 035 $a(SSID)ssj0000113901 035 $a(PQKBManifestationID)11141585 035 $a(PQKBTitleCode)TC0000113901 035 $a(PQKBWorkID)10101802 035 $a(PQKB)10252285 035 $a(UkCbUP)CR9780511536717 035 $a(MiAaPQ)EBC259893 035 $a(Au-PeEL)EBL259893 035 $a(CaPaEBR)ebr10130415 035 $a(CaONFJC)MIL47787 035 $a(OCoLC)935232706 035 $a(PPN)261306529 035 $a(EXLCZ)991000000000353231 100 $a20040115d2004 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBootstrap techniques for signal processing /$fAbdelhak M. Zoubir, D. Robert Iskander 205 $a1st ed. 210 $aCambridge ;$aNew York $cCambridge$d2004 215 $a1 online resource (xiv, 217 pages) $cdigital, PDF file(s) 300 $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). 311 $a0-521-03405-1 311 $a0-521-83127-X 320 $aIncludes bibliographical references (p. 201-214) and index. 327 $aCover; Half-title; Title; Copyright; 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; Appendix 2 Bootstrap Matlab Toolbox; References; Index 330 $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. 606 $aSignal processing$xMathematics 606 $aImage processing$xMathematics 606 $aBootstrap (Statistics) 615 0$aSignal processing$xMathematics. 615 0$aImage processing$xMathematics. 615 0$aBootstrap (Statistics) 676 $a621.382/2 700 $aZoubir$b Abdelhak M$01653859 701 $aIskander$b D. Robert$01756733 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910818335603321 996 $aBootstrap techniques for signal processing$94194195 997 $aUNINA