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Using wavelets for data smoothing: A simulation study

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  • Graham Horgan

Abstract

Wavelet shrinkage has been proposed as a highly adaptable approach to signal smoothing, which can produce optimum results in some senses. This paper examines the performance of the method as a function of its parameters, by simulation for time series showing gradual, rapid and discontinuous variations, for a range of signal-to-noise ratios. Some general conclusions are drawn. The effects of the choice of wavelet, choice of threshold and choice of resolution cut-off are considered. The use of the residual autocorrelation as a diagnostic tool is suggested.

Suggested Citation

  • Graham Horgan, 1999. "Using wavelets for data smoothing: A simulation study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(8), pages 923-932.
    • Handle: RePEc:taf:japsta:v:26:y:1999:i:8:p:923-932
    DOI: 10.1080/02664769921936
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    References listed on IDEAS

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    1. Y. Wang, 1997. "Fractal Function Estimation via Wavelet Shrinkage," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(3), pages 603-613.
    2. Iain M. Johnstone & Bernard W. Silverman, 1997. "Wavelet Threshold Estimators for Data with Correlated Noise," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(2), pages 319-351.
    3. Hall, Peter & Nason, Guy P., 1997. "On choosing a non-integer resolution level when using wavelet methods," Statistics & Probability Letters, Elsevier, vol. 34(1), pages 5-11, May.
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    Cited by:

    1. Tim Ramsay, 2002. "Spline smoothing over difficult regions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 307-319, May.
    2. Mehmetcik Bayazit & Hafzullah Aksoy, 2001. "Using wavelets for data generation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(2), pages 157-166.
    3. Shucong Liu & Hongjun Wang & Rui Li & Beilei Ji, 2022. "A Novel Feature Identification Method of Pipeline In-Line Inspected Bending Strain Based on Optimized Deep Belief Network Model," Energies, MDPI, vol. 15(4), pages 1-19, February.

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