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Wavelet cross-correlation sequence estimates using the maximal overlap discrete wavelet transform (MODWT)

returns
the wavelet cross-correlation sequence estimates for the maximal overlap
discrete wavelet transform (MODWT) transforms specified in `xcseq`

= modwtxcorr(`w1`

,`w2`

)`w1`

and `w2`

. `xcseq`

is
a cell array of vectors where the elements in each cell correspond
to cross-correlation sequence estimates. If there are enough nonboundary
coefficients at the final level, `modwtxcorr`

returns
the scaling cross-correlation sequence estimate in the final cell
of `xcseq`

.

`[___] = modwtxcorr(___,'reflection')`

reduces
the number of wavelet and scaling coefficients at each scale by half
before computing the cross-correlation sequences. Specifying the `'reflection'`

option
in `modwtxcorr`

is equivalent to first obtaining
the MODWT of `w1`

`w2`

with `'periodic'`

boundary
handling and then computing the wavelet cross-correlation sequence
estimates. Use this option only when you obtain the MODWT of `w1`

and `w2`

using
the `'reflection'`

boundary condition. You must enter
the entire character vector `'reflection'`

. If you
added a wavelet named `'reflection'`

using the wavelet
manager, you must rename that wavelet prior to using this option. `'reflection'`

may
be placed in any position in the input argument list after the input
transforms `w1`

`w2`

.

[1] Percival, D. B., and A. T. Walden. *Wavelet Methods for Time Series
Analysis*. Cambridge, UK: Cambridge University Press, 2000.

[2] Whitcher, B., P. Guttorp, and D. B. Percival. "Wavelet
analysis of covariance with application to atmospheric time series." *Journal
of Geophysical Research*, Vol. 105, 2000, pp. 14941–14962.