Wavelet Thresholding via a Bayesian

  title={Wavelet Thresholding via a Bayesian},
  author={ApproachF. Abramovich and Theofanis Sapatinas and B. W. Silverman},
We discuss a Bayesian formalism which gives rise to a type of wavelet threshold estimation in non-parametric regression. A prior distribution is imposed on the wavelet coeecients of the unknown response function , designed to capture the sparseness of wavelet expansion common to most applications. For the prior speciied, the posterior median yields a thresholding procedure. Our prior model for the underlying function can be adjusted to give functions falling in any speciic Besov space. We… CONTINUE READING
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