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  3. Bootstrap Techniques for Signal Processing
  • Cited by 127
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    • Publisher:
      Cambridge University Press
      Publication date:
      December 2009
      May 2004
      ISBN:
      9780511536717
      9780521831277
      9780521034050
      DOI:
      https://doi.org/10.1017/CBO9780511536717
      Dimensions:
      (247 x 174 mm)
      Weight & Pages:
      0.625kg, 232 Pages
      Dimensions:
      (247 x 174 mm)
      Weight & Pages:
      0.373kg, 232 Pages
      • Subjects:
      Communications and Signal Processing, Engineering, Statistics for Physical Sciences and Engineering, Statistics and Probability
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    Subjects:
    Communications and Signal Processing, Engineering, Statistics for Physical Sciences and Engineering, Statistics and Probability
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    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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