Mcmc Methods in Wavelet Shrinkage: Non-equally Spaced Regression, Density and Spectral Density Estimation

@inproceedings{Vidakovic1999McmcMI,
  title={Mcmc Methods in Wavelet Shrinkage: Non-equally Spaced Regression, Density and Spectral Density Estimation},
  author={B. Vidakovic},
  year={1999}
}
We consider posterior inference in wavelet based models for non-parametric regression with unequally spaced data, density estimation and spectral density estimation. The common theme in all three applications is the lack of posterior independence for the wavelet coe cients djk . In contrast, most commonly considered applications of wavelet decompositions in Statistics are based on a setup which implies a posteriori independent coe cients, essentially reducing the inference problem to a series… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 31 references

Bayesian model choice via Markov chain Monte Carlo

  • B. P. Carlin, S. Chib
  • Journal of the Royal Statistical Society, Series…
  • 1995
Highly Influential
7 Excerpts

Bayesian wavelet estiamtion of a spectral density

  • P. uller
  • 1999
Highly Influential
4 Excerpts

Introduction toWavelets, in Bayesian

  • P. uller
  • 1999
Highly Influential
4 Excerpts

Spectral view of wavelets and nonlinear regression, in Bayesian Inference in Wavelet Based Models, P. M

  • S. Marron
  • 1999
Highly Influential
4 Excerpts

Wavelet nonparametric regression using basis averaging, in Bayesian Inference in Wavelet Based Models, P. M

  • P. Yau, R. Kohn
  • 1999
1 Excerpt

Adaptive Bayesian Wavelet Shrinkage

  • H. Chipman, E. Kolaczyk, R. McCulloch
  • Journal of the American Statistical Association,
  • 1997

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