References

A COMPARISON SIMULATION STUDY OF STANDARD WAVELET SHRINKAGE METHODS IN NONPARAMETRIC REGRESSION MODELS WITH POSITIVE NOISE


[1] F. Abramovich and Y. Benjamini, Adaptive thresholding of wavelet coefficients, Computational Statistics & Data Analysis 22(4) (1996), 351-361.
DOI: https://doi.org/10.1016/0167-9473(96)00003-5

[2] A. Antoniadis, D. Leporini and J. C. Pesquet, Wavelet thresholding for some classes of non-Gaussian noise, Statistica Neerlandica 56(4) (2002), 434-453.
DOI: https://doi.org/10.1111/1467-9574.00211

[3] R. Averkamp and C. Houdré, Wavelet thresholding for non-necessarily Gaussian noise: Idealism, The Annals of Statistics 31(1) (2003), 110-151.
DOI: https://doi.org/10.1214/aos/1046294459

[4] L. Cutillo, Y. Y. Jung, F. Ruggeri and B. Vidakovic, Larger posterior mode wavelet thresholding and applications, Journal of Statistical Planning and Inference 138(12) (2008), 3758-3773.
DOI: https://doi.org/10.1016/j.jspi.2007.12.015

[5] I. Daubechies, Ten Lectures on Wavelets, SIAM, Philadelphia, 1992.
DOI: https://doi.org/10.1137/1.9781611970104

[6] D. L. Donoho, Nonlinear Wavelet Methods of Recovery for Signals, Densities, and Spectra from Indirect and Noisy Data, Proceedings of Symposia in Applied Mathematics, Volume 47, American Mathematical Society, Providence: RI, 1993.

[7] D. L. Donoho, Unconditional bases are optimal bases for data compression and statistical estimation, Applied and Computational Harmonic Analysis 1(1) (1993), 100-115.
DOI: https://doi.org/10.1006/acha.1993.1008

[8] D. L. Donoho, De-noising by soft-thresholding, IEEE Transactions on Information Theory 41(3) (1995), 613-627.
DOI: https://doi.org/10.1109/18.382009

[9] D. L. Donoho, Nonlinear solution of linear inverse problems by wavelet-vaguelette decomposition, Applied and Computational Harmonic Analysis 2(2) (1995), 101-126.
DOI: https://doi.org/10.1006/acha.1995.1008

[10] D. L. Donoho and I. M. Johnstone, Ideal denoising in an orthonormal basis chosen from a library of bases, Comptes Rendus de l’Académie des Sciences: Paris A 319 (1994), 1317-1322.

[11] D. L. Donoho and I. M. Johnstone, Ideal spatial adaptation by wavelet shrinkage, Biometrika 81(3) (1994), 425-455.
DOI: https://doi.org/10.2307/2337118

[12] D. L. Donoho and I. M. Johnstone, Adapting to unknown smoothness via wavelet shrinkage, Journal of the American Statistical Association 90(432) (1995), 1200-1224.

[13] M. A. T. Figueiredo and R. D. Nowak, Wavelet-based image estimation: An empirical Bayes approach using Jeffrey’s noninformative prior, IEEE Transactions on Image Processing 10(9) (2001), 1322-1331.
DOI: https://doi.org/10.1109/83.941856

[14] M. Jansen, Noise Reduction by Wavelet Thresholding, Springer, New York, 2001.

[15] I. M. Johnstone and B. W. Silverman, Empirical Bayes selection of wavelet thresholds, The Annals of Statistics 33(4) (2005), 1700-1752.
DOI: https://doi.org/10.1214/009053605000000345

[16] D. Leporini and J. C. Pesquet, Bayesian wavelet denoising: Besov priors and non-Gaussian noises, Signal Processing 81(1) (2001), 55-67.
DOI: https://doi.org/10.1016/S0165-1684(00)00190-0

[17] S. G. Mallat, A Wavelet Tour of Signal Processing, Academic Press, San Diego, 1998.

[18] P. A. de Morettin, Ondas e Ondaletas: Da Análise de Fourier á Análise de Ondaletas de Séries Temporais, 2nd Edition, São Paulo, University of São Paulo Press, 2014.

[19] G. P. Nason, Wavelet shrinkage using cross-validation, Journal of the Royal Statistical Society: Series B (Methodological) 58(2) (1996), 463-479.
DOI: https://doi.org/10.1111/j.2517-6161.1996.tb02094.x

[20] G. P. Nason, Wavelet Methods in Statistics with R, Springer, 2008.

[21] M. H. Neumann and R. von Sachs, Wavelet Thresholding: Beyond the Gaussian I. I. D. Situation, Wavelets and Statistics, Springer, 1995.

[22] A. R. S. dos Sousa and N. L. Garcia, Wavelet Shrinkage in Nonparametric Regression Models with Positive Noise (2021), arXiv:2109.06102.

[23] B. Vidakovic, Statistical Modeling by Wavelets, Wiley, New York, 1999.
DOI: https://doi.org/10.1002/9780470317020

[24] B. Vidakovic and F. Ruggeri, BAMS method: Theory and simulations, Sankhya: The Indian Journal of Statistics: Series B 63(2) (2001), 234-249.