*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
The problem of estimating the log-spectrum of a
stationary Gaussian time series by Bayesianly induced
shrinkage of empirical wavelet coefficients is studied.
A model in the wavelet domain that
accounts for distributional properties of the log-periodogram
at levels of fine detail and approximate normality at
coarse levels in the wavelet decomposition, is proposed.
The smoothing procedure, called BAMS-LP (Bayesian Adaptive Multiscale
Shrinker of Log-Periodogram),
ensures that the reconstructed log-spectrum is
as noise-free as possible. It is also shown that the resulting
Bayes estimators are asymptotically optimal (in the frequentist sense).
Comparisons with non-wavelet and wavelet-non-Bayesian
methods are discussed.
This is a joint work with Marianna Pensky from UCF.
