Adaptive Bayesian Wavelet Shrinkage

@article{Chipman1997AdaptiveBW,
  title={Adaptive Bayesian Wavelet Shrinkage},
  author={H. Chipman and E. Kolaczyk and R. McCulloch},
  journal={Journal of the American Statistical Association},
  year={1997},
  volume={92},
  pages={1413-1421}
}
  • H. Chipman, E. Kolaczyk, R. McCulloch
  • Published 1997
  • Mathematics
  • Journal of the American Statistical Association
  • Abstract When fitting wavelet based models, shrinkage of the empirical wavelet coefficients is an effective tool for denoising the data. This article outlines a Bayesian approach to shrinkage, obtained by placing priors on the wavelet coefficients. The prior for each coefficient consists of a mixture of two normal distributions with different standard deviations. The simple and intuitive form of prior allows us to propose automatic choices of prior parameters. These parameters are chosen… CONTINUE READING
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