Bootstrap Techniques for Signal Processing
Iskander, D. Robert & Zoubir, Abdelhak M. (2004) Bootstrap Techniques for Signal Processing. Cambridge University Press, Cambridge, UK..
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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|Additional Information:||For more information about this book please refer to the publisher's website (see link) or contact the author. Author contact details : firstname.lastname@example.org|
|Divisions:||Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
|Copyright Owner:||Copyright 2004 Cambridge University Press|
|Deposited On:||04 Apr 2007 00:00|
|Last Modified:||29 Feb 2012 13:08|
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