Description Usage Arguments Value Author(s) References See Also
Runs an iterative coordinate descent algorithm to minimize the SURE for mode-specific soft thresholding estimators.
1 2 | soft_coord(c_obj, lambda_init, c_init, itermax = 1000, tol = 10^-4,
print_iter = TRUE, tau2 = 1, use_sure = TRUE)
|
c_obj |
The output from |
lambda_init |
A vector of numerics of length n. The initial starting values for the thresholding parameters. |
c_init |
A positive numeric. The starting value of the scaling parameter. |
itermax |
A positive integer. The maximum number of Newton steps to iterate through. |
tol |
A positive numeric. The stopping criterion. |
print_iter |
A logical. Should we print the updates of the Newton Step? |
tau2 |
A positive numeric. The variance. Assumed known and defaults to 1. |
use_sure |
A logical. Which stopping criterion should we use? The mean
absolute difference in the parameters ( |
c
A numeric. The final value of the scaling parameter.
lambda
A vector of numerics. The final values of the thresholding
parameters.
est
An array of numerics. The final mean estimate.
David Gerard.
Gerard, D., & Hoff, P. (2015). Adaptive Higher-order Spectral Estimators. arXiv preprint arXiv:1505.02114.
update_c
, update_lambda
,
get_c
.
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