Wavelet shrinkage based on the raised cosine prior

Fuente: arXiv
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Main Authors: Reina, Juliana Marchesi, Sousa, Alex Rodrigo dos Santos
Format: Preprint
Published: 2025
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author Reina, Juliana Marchesi
Sousa, Alex Rodrigo dos Santos
author_facet Reina, Juliana Marchesi
Sousa, Alex Rodrigo dos Santos
contents We propose a Bayesian shrinkage rule to estimate the wavelet coefficients in a nonparametric regression model with Gaussian errors, based on a mixture of a point mass function at zero and a symmetric, zero-centered raised cosine distribution prior. The proposed rule outperformed established shrinkage and thresholding methods in specific scenarios of signal-to-noise ratio and sample size values in conducted simulation studies involving the so-called Donoho and Johnstone test functions. Statistical properties of the rule, such as squared bias, variance, and risks, are analyzed, and two illustrations in real datasets are provided.
format Preprint
id arxiv_https___arxiv_org_abs_2507_10794
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Wavelet shrinkage based on the raised cosine prior
Reina, Juliana Marchesi
Sousa, Alex Rodrigo dos Santos
Methodology
We propose a Bayesian shrinkage rule to estimate the wavelet coefficients in a nonparametric regression model with Gaussian errors, based on a mixture of a point mass function at zero and a symmetric, zero-centered raised cosine distribution prior. The proposed rule outperformed established shrinkage and thresholding methods in specific scenarios of signal-to-noise ratio and sample size values in conducted simulation studies involving the so-called Donoho and Johnstone test functions. Statistical properties of the rule, such as squared bias, variance, and risks, are analyzed, and two illustrations in real datasets are provided.
title Wavelet shrinkage based on the raised cosine prior
topic Methodology
url https://arxiv.org/abs/2507.10794