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An Overview of Wavelet Regularization

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Bayesian Inference in Wavelet-Based Models

Part of the book series: Lecture Notes in Statistics ((LNS,volume 141))

Abstract

This chapter reviews the construction of wavelet thresholding (shrinkage) estimators by regularization. Both penalty and maximum a posterori approaches are presented and their relation is discussed.

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References

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Authors
  1. Yazhen Wang
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Editor information

Editors and Affiliations

  1. Institute of Statistics and Decision Sciences, Duke University, Box 90251, Durham, 27708-0251, NC, England

    Peter Müller  & Brani Vidakovic  & 

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© 1999 Springer Science+Business Media New York

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Wang, Y. (1999). An Overview of Wavelet Regularization. In: Müller, P., Vidakovic, B. (eds) Bayesian Inference in Wavelet-Based Models. Lecture Notes in Statistics, vol 141. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0567-8_8

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  • DOI: https://doi.org/10.1007/978-1-4612-0567-8_8

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-98885-6

  • Online ISBN: 978-1-4612-0567-8

  • eBook Packages: Springer Book Archive

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