Description Usage Arguments Details Value Author(s) Examples
Computes the Hadamard Variance
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The decomposition and the amount of time it takes to perform it depends on whether you are using the Tau Overlap or the Maximal Overlap.
Maximal Overlap Hadamard Variance Given N equally spaced samples with averaging time tau = n*tau_0, where n is an integer such that 1<= n <= N/3. Therefore, n is able to be selected from {n|n< floor(log3(N))} Then, M = N - 3n samples exist. The Maximal-overlap estimator is given by: See PDF Manual
where See PDF Manual.
Tau-Overlap Hadamard Variance Given N equally spaced samples with averaging time tau = n*tau_0, where n is an integer such that 1<= n <= N/2. Therefore, n is able to be selected from {n|n< floor(log2(N))} Then, a sampling of m = ≤ft\lfloor {\frac{{N - 1}}{n}} \right\rfloor - 1 samples exist. The tau-overlap estimator is given by: See PDF Manual where See PDF Manual.
Hadamard variance fixed
hadam A list
that contains:
"clusters"The size of the cluster
"hadamard"The Hadamard variance
"errors"The error associated with the variance estimation.
Avinash Balakrishnan, JJB
1 2 3 4 5 6 7 8 9 10 11 | set.seed(999)
# Simulate white noise (P 1) with sigma^2 = 4
N = 100000
white.noise = rnorm(N, 0, 2)
#plot(white.noise,ylab="Simulated white noise process",xlab="Time",type="o")
#Simulate random walk (P 4)
random.walk = cumsum(0.1*rnorm(N, 0, 2))
combined.ts = white.noise+random.walk
hadam_mo = hadam(combined.ts)
hadam_to = hadam(combined.ts, type = "to")
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