wn_to_wv: Gaussian White Noise to WV
In schoi355/gmwm: Generalized Method of Wavelet Moments
wn_to_wv R Documentation
Gaussian White Noise to WV
Description
This function compute the Haar WV of a Gaussian White Noise process
Usage
wn_to_wv(sigma2, tau)
Arguments
sigma2
A double corresponding to variance of WN
tau
A vec containing the scales e.g. 2^tau
Value
A vec containing the wavelet variance of the white noise.
Process Haar Wavelet Variance Formula
The Gaussian White Noise (WN) process has a Haar Wavelet Variance given by:
nu[j]^2 (sigma2) = (sigma2)/(tau[j]^2)
Haar Wavelet Derivation Information
For more information, please see: Supported Haar Wavelet Formulae (Internet Connection Required).
Examples
ntau = 8
tau = 2^(1:ntau)
wv.theo = wn_to_wv(1, tau)
schoi355/gmwm documentation built on April 11, 2022, 1:21 a.m.
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| wn_to_wv | R Documentation |
Gaussian White Noise to WV
Description
This function compute the Haar WV of a Gaussian White Noise process
Usage
wn_to_wv(sigma2, tau)
Arguments
sigma2 |
A |
tau |
A |
Value
A vec containing the wavelet variance of the white noise.
Process Haar Wavelet Variance Formula
The Gaussian White Noise (WN) process has a Haar Wavelet Variance given by:
nu[j]^2 (sigma2) = (sigma2)/(tau[j]^2)
Haar Wavelet Derivation Information
For more information, please see: Supported Haar Wavelet Formulae (Internet Connection Required).
Examples
ntau = 8 tau = 2^(1:ntau) wv.theo = wn_to_wv(1, tau)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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