A wavelet packet tree, from the discrete wavelet packet transform (DWPT), is tested nodebynode for white noise. This is the first step in selecting an orthonormal basis for the DWPT.
1 2 3 4  cpgram.test(y, p = 0.05, taper = 0.1)
css.test(y)
entropy.test(y)
portmanteau.test(y, p = 0.05, type = "BoxPierce")

y 
wavelet packet tree (from the DWPT) 
p 
significance level 
taper 
weight of cosine bell taper ( 
type 

Topdown recursive testing of the wavelet packet tree is
Boolean vector of the same length as the number of nodes in the wavelet packet tree.
B. Whitcher
Brockwell and Davis (1991) Time Series: Theory and Methods, (2nd. edition), SpringerVerlag.
Brown, Durbin and Evans (1975) Techniques for testing the constancy of regression relationships over time, Journal of the Royal Statistical Society B, 37, 149163.
Percival, D. B., and A. T. Walden (1993) Spectral Analysis for Physical Applications: Multitaper and Conventional Univariate Techniques, Cambridge University Press.
1 2 3 4 5 6 7 8 9 10  data(mexm)
J < 6
wf < "la8"
mexm.dwpt < dwpt(mexm[(1:4)], wf, J)
## Not implemented yet
## plot.dwpt(x.dwpt, J)
mexm.dwpt.bw < dwpt.brick.wall(mexm.dwpt, wf, 6, method="dwpt")
mexm.tree < ortho.basis(portmanteau.test(mexm.dwpt.bw, p=0.025))
## Not implemented yet
## plot.basis(mexm.tree)

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