Deterministic/Stochastic Wavelet Decomposition for Recovery of Signal From Noisy Data

@article{Huang2000DeterministicStochasticWD,
  title={Deterministic/Stochastic Wavelet Decomposition for Recovery of Signal From Noisy Data},
  author={H.-C. Huang and Noel Cressie},
  journal={Technometrics},
  year={2000},
  volume={42},
  pages={262-276}
}
In a series of recent papers on nonparametric regression, D. Donoho and I. Johnstone developed wavelet shrinkage methods for recovering unknown piecewise-smooth deterministic signals from noisy data. Wavelet shrinkage based on the Bayesian approach involves specifying a prior distribution on the wavelet coeecients, which is usually assumed to have a distribution with zero mean. There is no a priori reason why all prior means should be zero; indeed, one can imagine certain types of signals where… CONTINUE READING
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