Computes the Forward WaveD Transform.
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y |
Sample of f*g + (Gaussian noise), a vector of dyadic length (i.e. 2^(J-1) where J is the largest resolution level). Here f is the target function, g is the convolution kernel. |
g |
Sample of g or g + (Gaussian noise), same length as yobs. The default is the Dirac mass at 0. |
L |
Lowest resolution level; the default is 3. |
deg |
The degree of the Meyer wavelet, either 1, 2, or 3 (the default). |
F |
Finest resolution level; the default is the data-driven choice j1 (see Value below). |
thr |
A vector of length F-L+1, giving thresholds at each resolution levels L,L+1,...,F; default is maxiset threshold. |
SOFT |
if SOFT=TRUE, uses the soft thresholding policy as opposed to the hard (SOFT=FALSE, the default). |
Returns a vector of wavelet coefficients of length n (the same length as y), the last n/2 entries are wavelet coefficients at resolution level J-1, where J= log_2(n); the n/4 entries before that are the wavelet coefficients at resolution level J-2, and so on until level L. In addition the 2^L entries are scaling coefficients at coarse level C=L.
Johnstone, I., Kerkyacharian, G., Picard, D. and Raimondo, M. (2004), 'Wavelet deconvolution in a periodic setting', Journal of the Royal Statistical Society, Series B 66(3),547–573. with discussion pp.627–652.
Raimondo, M. and Stewart, M. (2006), ‘The WaveD Transform in R’, preprint, School and Mathematics and Statistics, University of Sydney.
WaveD
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