A transform of the data with respect to an expansion comprised of squared wavelets.
The sequence that you want transformed, of dyadic length.
A structure containing the necessary information to
transform the wavelet transform of the sequence to the
squared wavelet transform. This is provided by the
This function first computes the wavelet transform of the
data. Then, level by level it is retransformed into the
coefficients of the squared-wavelet transform using the
structure. Fine levels, that cannot be computed using the
approximate method are computed directly by the brute-force method
sqndwdecomp. Method used is described in Fryzlewicz, Nason and
von Sachs (2008), and is analogous to the ‘powers of wavelets’
transform described in Herrick (2000) and Barber, Nason and
An object of class
wd containing the non-decimated
squared wavelet transform.
Barber, S., Nason, G.P. and Silverman, B.W. (2002) Posterior probability intervals for wavelet thresholding. J. R. Statist. Soc. B, 64, 189-206.
Fryzlewicz, P., Nason, G.P. and von Sachs, R. (2008) A wavelet-Fisz approach to spectrum estimation. J. Time Ser. Anal., 29, 868-880.
Herrick, D.R.M. (2000) Wavelet Methods for Curve Estimation, PhD thesis, University of Bristol, U.K.
Nason, G.P. and Savchev, D. (2014) White noise testing using wavelets. Stat, 3, 351-362. http://dx.doi.org/10.1002/sta4.69
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