View source: R/SURE_MSEthresh.R
SURE_MSEthresh  R Documentation 
Adaptive Threshold Selection Using Principle of SURE with the inclusion of Mean Squared Error (MSE) for comparison.
SURE_MSEthresh(
wcn,
wcf,
thresh,
diagWWt,
beta = 2,
sigma,
hatsigma = NA,
policy = "uniform",
keepwc = TRUE
)
wcn 
Numeric vector of the noisy spectral graph wavelet coefficients. 
wcf 
Numeric vector of the true spectral graph wavelet coefficients. 
thresh 
Numeric vector of threshold values. 
diagWWt 
Numeric vector of weights typically derived from the diagonal elements of the wavelet frame matrix. 
beta 
A numeric value specifying the type of thresholding to be used:

sigma 
A numeric value representing the standard deviation (sd) of the noise. 
hatsigma 
An optional numeric value providing an estimate of the noise standard deviation (default is NA). 
policy 
A character string determining the thresholding policy. Valid options include:

keepwc 
A logical value determining if the thresholded wavelet coefficients should be returned (Default is TRUE). 
SURE_MSEthresh
function extends the SUREthresh
function by providing an MSE between the true coefficients and their thresholded versions for a given thresholding function h
. This allows for a more comprehensive evaluation of the denoising quality in simulated scenarios where the true function is known.
A list containing:
A dataframe with calculated MSE, SURE, and hatSURE values.
Minima of SURE, hatSURE, and MSE, and their corresponding optimal thresholds.
Thresholded wavelet coefficients (if keepwc = TRUE
).
Donoho, D. L., & Johnstone, I. M. (1995). Adapting to unknown smoothness via wavelet shrinkage. Journal of the american statistical association, 90(432), 12001224.
de Loynes, B., Navarro, F., Olivier, B. (2021). Datadriven thresholding in denoising with Spectral Graph Wavelet Transform. Journal of Computational and Applied Mathematics, Vol. 389.
Stein, C. M. (1981). Estimation of the mean of a multivariate normal distribution. The annals of Statistics, 11351151.
SUREthresh
, GVN
, HPFVN
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