This function might be better called using the regular
threshold function using the op2 policy.
Corresponds to the wavelet thresholding routine developed by Ogden and Parzen (1994) Data dependent wavelet thresholding in nonparametric regression with change-point applications. Tech Rep 176, University of South Carolina, Department of Statistics.
TOthreshda2(ywd, alpha = 0.05, verbose = FALSE, return.threshold = FALSE)Returns the threshold value if return.threshold==TRUE otherwise
returns the shrunk set of wavelet coefficients.
The wd.object that you wish to threshold.
The smoothing parameter which is a p-value
Whether messages get printed
If TRUE then the threshold value gets returned rather than the actual thresholded object
Todd Ogden
The TOthreshda2 method operates in a similar fashion to
TOthreshda1 except that it takes the cumulative sum
of squared coefficients, creating a sample "Brownian bridge" process,
and then using the standard Kolmogorov-Smirnov statistic in testing.
In this situation, the level of the hypothesis tests, alpha, has default value 0.05. Note that the choice of alpha controls the smoothness of the resulting wavelet estimator -- in general, a relatively large alpha makes it easier to include coefficients, resulting in a more wiggly estimate; a smaller alpha will make it more difficult to include coefficients, yielding smoother estimates.
threshold,TOthreshda1, wd