varip2 | R Documentation |
Performs a direct computation of an estimate of the variance of the Haar wavelet coefficients of the raw wavelet periodogram of a time series.
varip2(i, p, ll, S, P)
i |
Scale parameter of Haar wavelet analyzing periodogram. Scale 1 is the finest scale. |
p |
Location parameter of Haar wavelet analyzing periodogram |
ll |
Scale of the raw wavelet periodogram being analyzed |
S |
Estimate of the spectrum, under the assumption of stationarity.
So, this is just a vector of (possibly) J scales (which is often
the usual spectral estimate averaged over time). Note: that the
main calling function, |
P |
Is an autocorrelation wavelet object, returned by the
|
Computes the variance of the Haar wavelet coefficients of the raw wavelet periodogram. Note, that this is merely an estimate of the variances.
A list with the following components:
covAA |
A component of the variance |
covAB |
A component of the variance |
covBB |
A component of the variance |
ans |
The actual variance |
Guy Nason.
Nason, G.P. (2013) A test for second-order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series. J. R. Statist. Soc. B, 75, 879-904. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssb.12015")}
Cvarip2
,hwtos2
, covIwrap
#
# Generate autocorrelation wavelets
#
P1 <- PsiJ(-5, filter.number=1, family="DaubExPhase")
#
#
# Now compute varip2: this is the variance of the Haar wavelet coefficient
# at the finest scale, location 10 and P1 autocorrelation wavelet.
# Note, I've used S to be the exact coefficients which would be for white noise.
# In practice, S would be an *estimate* calculated from the data.
#
varip2(i=1, p=10, ll=2, S=c(1/2, 1/4, 1/8, 1/16, 1/32), P=P1)
#
# Ans component is 1.865244
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