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  3. Bootstrap Techniques for Signal Processing
  • Cited by 127
Publisher:
Cambridge University Press
Online publication date:
December 2009
Print publication year:
2004
Online ISBN:
9780511536717
DOI:
https://doi.org/10.1017/CBO9780511536717
Subjects:
Communications and Signal Processing, Engineering, Statistics for Physical Sciences and Engineering, Statistics and Probability
Information

Book description

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.

Reviews

'… an excellent text for a graduate level engineering course or a useful reference for those who wish to apply bootstrap techniques in their work.'

Source: Annals of Biomedical Engineering

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