Wavelet Shrinkage via Thresholding
Perform wavelet shrinkage using data-analytic, hybrid SURE, manual, SURE, or universal thresholding.
da.thresh(wc, alpha = .05, max.level = 4, verbose = FALSE, return.thresh = FALSE) hybrid.thresh(wc, max.level = 4, verbose = FALSE, seed = 0) manual.thresh(wc, max.level = 4, value, hard = TRUE) sure.thresh(wc, max.level = 4, hard = TRUE) universal.thresh(wc, max.level = 4, hard = TRUE) universal.thresh.modwt(wc, max.level = 4, hard = TRUE)
wc |
wavelet coefficients |
alpha |
level of the hypothesis tests |
max.level |
maximum level of coefficients to be affected by threshold |
verbose |
if |
value |
threshold value (only utilized in |
hard |
Boolean value, if |
seed |
sets random seed (only utilized in |
return.thresh |
if |
An extensive amount of literature has been written on wavelet shrinkage. The functions here represent the most basic approaches to the problem of nonparametric function estimation. See the references for further information.
The default output is a list structure, the same length as was input, containing only those wavelet coefficients surviving the threshold.
B. Whitcher (some code taken from R. Todd Ogden)
Gencay, R., F. Selcuk and B. Whitcher (2001) An Introduction to Wavelets and Other Filtering Methods in Finance and Economics, Academic Press.
Ogden, R. T. (1996) Essential Wavelets for Statistical Applications and Data Analysis, Birkhauser.
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis, Cambridge University Press.
Vidakovic, B. (1999) Statistical Modeling by Wavelets, John Wiley \& Sons.
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