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On choosing a non-integer resolution level when using wavelet methods

Author

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  • Hall, Peter
  • Nason, Guy P.

Abstract

In curve estimation using wavelet methods it is common to select the resolution level to be an integer, so as to exploit the computational advantages of the pyramid or cascade algorithm. This choice, however, can produce a noticeable amount of either oversmoothing or undersmoothing. Its analogue for estimation by kernel methods is to restrict the bandwidth to be an integer power of , which would seldom be acceptable. In this note we quantify the advantages of non-integer resolution levels.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:stapro:v:34:y:1997:i:1:p:5-11
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    Cited by:

    1. J. Christopher Westland & Jian Mou & Dafei Yin, 2018. "Prediction of Shared Bicycle Demand with Wavelet Thresholding," Papers 1802.02683, arXiv.org.
    2. Graham Horgan, 1999. "Using wavelets for data smoothing: A simulation study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(8), pages 923-932.
    3. Zhou, Yong & Wan, Alan T.K. & Xie, Shangyu & Wang, Xiaojing, 2010. "Wavelet analysis of change-points in a non-parametric regression with heteroscedastic variance," Journal of Econometrics, Elsevier, vol. 159(1), pages 183-201, November.

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