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'Analytic' wavelet thresholding

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  • Sofia C. Olhede
  • Andrew T. Walden

Abstract

We introduce so-called analytic stationary wavelet transform thresholding where, using the discrete Hilbert transform, we create a complex-valued 'analytic' vector from which an amplitude vector is defined. Thresholding of a real-valued wavelet coefficient at some transform level is carried out according to the corresponding value in this amplitude vector; relevant statistical results follow from properties of the discrete Hilbert transform. Analytic stationary wavelet transform thresholding is found to produce consistently a reduced mean squared error compared to using standard stationary wavelet transform, or 'cycle spinning', thresholding. For signals with extensive oscillations at some transform levels, this improvement is very marked. Furthermore we show that our thresholding test is invariant to phase shifts in the data, whereas, if complex wavelet filters are being used, the filters must be analytic or anti-analytic at each level of the wavelet transform. Copyright 2004, Oxford University Press.

Suggested Citation

  • Sofia C. Olhede & Andrew T. Walden, 2004. "'Analytic' wavelet thresholding," Biometrika, Biometrika Trust, vol. 91(4), pages 955-973, December.
    • Handle: RePEc:oup:biomet:v:91:y:2004:i:4:p:955-973
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    File URL: http://hdl.handle.net/10.1093/biomet/91.4.955
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    Cited by:

    1. Fryzlewicz, Piotr, 2007. "Bivariate hard thresholding in wavelet function estimation," LSE Research Online Documents on Economics 25219, London School of Economics and Political Science, LSE Library.
    2. Jiang, Rong & Yan, Hong, 2008. "Studies of spectral properties of short genes using the wavelet subspace Hilbert–Huang transform (WSHHT)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(16), pages 4223-4247.

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