Wavelet Basis Selection for Regression by Cross-Validation

  • Seth A. Greenblatt
Part of the Advances in Computational Economics book series (AICE, volume 6)


Much of the attention paid to wavelets in the statistical literature has been in the area of nonparametric regression, smoothing, and denoising. Generally, the wavelet-based techniques involve finding an appropriate threshold level and using that threshold to either shrink or zero out wavelet coefficients, depending upon their sizes. Recently, a new technique for threshold determination based upon cross-validation has been introduced. In this study, we look at several criteria that may be used, in conjunction with cross-validation, to select the most appropriate wavelet basis for the problem at hand and then we apply the resulting technique to financial market data.


Discrete Wavelet Transform Wavelet Packet Wavelet Basis Nonparametric Regression Chirp Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer Science+Business Media Dordrecht 1997

Authors and Affiliations

  • Seth A. Greenblatt

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