Wavelet shrinkage based on the raised cosine prior
Abstract
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 certain 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.
Fields of Study & Subjects
Image and Signal Denoising Methods
Advanced Data Compression Techniques
Seismic Imaging and Inversion Techniques
Publication details
- Journal / source
- Journal of Statistical Computation and Simulation
- Publisher
- Informa UK Limited
- Volume
- 96
- Issue
- 11
- Pages
- 2683–2704
- Publication date
- 25 February 2026
- Language
- EN