Qualified Thresholds forWavelet Shrinkage

Robust wavelet shrinkage using robust selection of thresholds

Choice of Thresholds for Wavelet Shrinkage Estimate of the Spectrum

We study the problem of estimating the log spectrum of a stationary Gaussian time series by thresholding the empirical wavelet coe cients. We propose the use of thresholds t j;n depending on sample size n, wavelet basis and resolution level j. At ne resolution levels (j= 1; 2; :::), we propose

Wavelet shrinkage: unification of basic thresholding functions and thresholds

This work addresses the unification of some basic functions and thresholds used in nonparametric estimation of signals by shrinkage in the wavelet domain. The Soft and Hard thresholding functions are presented as degenerate smooth sigmoid based shrinkage functions. The shrinkage achieved by this new family of sigmoid based functions is then shown to be equivalent to a regularisation of wavelet ...

Contextual Hidden Markov Models forWavelet - domain Signal

Wavelet-domain Hidden Markov Models (HMMs) provide a powerful new approach for statistical model-ing and processing of wavelet coeecients. In addition to characterizing the statistics of individual wavelet coeecients, HMMs capture some of the key interactions between wavelet coeecients. However, as HMMs model an increasing number of wavelet coeecient interactions , HMM-based signal processing b...

Qualified Ramifications

We consider the problem of ramifications, i.e., indirect effects of actions, having exceptions. It is argued that straightforward minimization of abnormality is insufficient in this context. Taking a recent causality-based solution to the plain Ramification Problem as starting point, we develop an action theory that is shown to successfully address this amalgamation of Ramification and Qualific...