Flexible Signal Denoising via Flexible Empirical Bayes Shrinkage.

ABSTRACT: Signal denoising-also known as non-parametric regression-is often performed through shrinkage estimation in a transformed (e.g., wavelet) domain; shrinkage in the transformed domain corresponds to smoothing in the original domain. A key question in such applications is how much to shrink, or, equivalently, how much to smooth. Empirical Bayes shrinkage methods provide an attractive solution to this problem; they use the data to estimate a distribution of underlying "effects," hence automatically select an appropriate amount of shrinkage. However, most existing implementations of empirical Bayes shrinkage are less flexible than they could be-both in their assumptions on the underlying distribution of effects, and in their ability to handle heteroskedasticity-which limits their signal denoisi

SUBMITTER: Xing Z 

PROVIDER: S-EPMC10751020 | biostudies-literature | 2021 Jan-Dec

REPOSITORIES: biostudies-literature