We construct an estimation and de-noising procedure for an input signal perturbed by a continuous-time Gaussian noise, using the local and occupation times of Gaussian processes. The method relies on the almost-sure minimization of a Stein Unbiased Risk Estimator (SURE ) obtained through integration by parts on Gaussian space, and applied to shrinkage estimators which are constructed by soft and hard thresholding .
Using integration by parts on Gaussian space we construct a Stein Unbiased Risk Estimator (SURE) for the drift of Gaussian processes using their local and occupation times . By almost-sure minimization of the SURE risk of shrinkage estimators we derive an estimation and de-noising procedure for an input signal perturbed by a continuous-time Gaussian noise .
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We construct an estimation and de-noising procedure for an input signal perturbed by a continuous-time Gaussian noise, using the
Using integration by parts on Gaussian space we construct a Stein Unbiased Risk Estimator (SURE) for the drift of Gaussian processes using their
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of Gaussian processes. The method relies on the
. By
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a Stein Unbiased Risk Estimator (SURE ) obtained through integration by parts on Gaussian space, and applied to shrinkage estimators which are constructed by soft and hard thresholding
the SURE risk of shrinkage estimators we derive an estimation and de-noising procedure for an input signal perturbed by a continuous-time Gaussian noise