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- W4288282387 abstract "In wavelet shrinkage and thresholding, most of the standard techniques do not consider information that wavelet coefficients might be bounded, although information about bounded energy in signals can be readily available. To address this, we present a Bayesian approach for shrinkage of bounded wavelet coefficients in the context of non-parametric regression. We propose the use of a zero-contaminated beta distribution with a support symmetric around zero as the prior distribution for the location parameter in the wavelet domain in models with additive gaussian errors. The hyperparameters of the proposed model are closely related to the shrinkage level, which facilitates their elicitation and interpretation. For signals with a low signal-to-noise ratio, the associated Bayesian shrinkage rules provide significant improvement in performance in simulation studies when compared with standard techniques." @default.
- W4288282387 created "2022-07-28" @default.
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- W4288282387 date "2019-07-15" @default.
- W4288282387 modified "2023-10-15" @default.
- W4288282387 title "Bayesian Wavelet Shrinkage with Beta Priors" @default.
- W4288282387 doi "https://doi.org/10.48550/arxiv.1907.06606" @default.
- W4288282387 hasPublicationYear "2019" @default.
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