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CVTresh: R Package for Level-Dependent Cross-Validation Thresholding

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  • Kim, Donghoh
  • Oh, Hee-Seok

Abstract

The core of the wavelet approach to nonparametric regression is thresholding of wavelet coefficients. This paper reviews a cross-validation method for the selection of the thresholding value in wavelet shrinkage of Oh, Kim, and Lee (2006), and introduces the R package CVThresh implementing details of the calculations for the procedures. This procedure is implemented by coupling a conventional cross-validation with a fast imputation method, so that it overcomes a limitation of data length, a power of 2. It can be easily applied to the classical leave-one-out cross-validation and K-fold cross-validation. Since the procedure is computationally fast, a level-dependent cross-validation can be developed for wavelet shrinkage of data with various sparseness according to levels.

Suggested Citation

  • Kim, Donghoh & Oh, Hee-Seok, 2006. "CVTresh: R Package for Level-Dependent Cross-Validation Thresholding," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 15(i10).
    • Handle: RePEc:jss:jstsof:v:015:i10
    DOI: http://hdl.handle.net/10.18637/jss.v015.i10
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    References listed on IDEAS

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    1. Johnstone, Iain & Silverman, Bernard W., 2005. "EbayesThresh: R Programs for Empirical Bayes Thresholding," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i08).
    2. Antoniadis A. & Fan J., 2001. "Regularization of Wavelet Approximations," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 939-967, September.
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