Published

2022-01-01

Asymmetric Prior in Wavelet Shrinkage

Priori asimétrico en contracción de ondículas

DOI:

https://doi.org/10.15446/rce.v45n1.92567

Keywords:

Wavelet shrinkage, Nonparametric regression, Asymmetric beta distribution (en)
Contracción de las ondículas, Regresión no paramétrico, Distribución beta asimétrica (es)

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Authors

  • Alex Rodrigo dos Santos Sousa University of São Paulo

In bayesian wavelet shrinkage, the already proposed priors to wavelet coefficients are assumed to be symmetric around zero. Although this assumption is reasonable in many applications, it is not general. The present paper proposes the use of an asymmetric shrinkage rule based on the discrete mixture of a point mass function at zero and an asymmetric beta distribution as prior to the wavelet coefficients in a non-parametric regression model. Statistical properties such as bias, variance, classical and bayesian risks of the associated asymmetric rule are provided and performances of the proposed rule are obtained in simulation studies involving artificial asymmetric distributed coefficients and the Donoho-Johnstone test functions. Application in a seismic real dataset is also analyzed.

En la contracción de las ondículas bayesianas, se supone que los coeficientes a priori ya propuestos de las ondículas son simétricos alrededor de cero. Aunque esta suposición es razonable en muchas aplicaciones, no
es general. El presente artículo propone el uso de una regla de contracción asimétrica basada en la mezcla discreta de una función de masa puntual en cero y una distribución beta asimétrica como priori de los coeficientes de ondícula en un modelo de regresión no paramétrico. Se proporcionan propiedades estadísticas tales como sesgo, varianza, riesgos clásicos y bayesianos de la regla asimétrica asociada y se obtienen los rendimientos de la regla propuesta en estudios de simulación que involucran coeficientes distribuidos asimétricos artificiales y las funciones de prueba de Donoho-Johnstone. También se analiza la aplicación en un conjunto de datos sísmicos reales.

References

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

Abramovich, F., Sapatinas, T. & Silverman, B. (1998), ‘Wavelet thresholding via a bayesian approach’, Royal Statistical Society pp. 725–749. DOI: https://doi.org/10.1111/1467-9868.00151

Angelini, C. & Vidakovic, B. (2004), ‘Gama-minimax wavelet shrinkage: a robust incorporation of information about energy of a signal in denoising applications’, Statistica Sinica (14), 103–125.

Antoniadis, A., Bigot, J. & Sapatinas, T. (2001), ‘Wavelet estimators in nonparametric regression: a comparative simulation study’, Journal of Statistical Software (6), 1–83. DOI: https://doi.org/10.18637/jss.v006.i06

Beenamol, M., Prabavathy, S. & Mohanalin, J. (2012), ‘Wavelet based seismic signal de-noising using shannon and tsallis entropy’, Computers and Mathematics with Applications (64), 3580–3593. DOI: https://doi.org/10.1016/j.camwa.2012.09.009

Bhattacharya, A., Pati, D., Pillai, N. & Dunson, D. (2015), ‘Dirichlet-laplace priors for optimal shrinkage’, Journal of the American Statistical Association (110), 1479–1490. DOI: https://doi.org/10.1080/01621459.2014.960967

Chipman, H., Kolaczyk, E. & McCulloch, R. (1997), ‘Adaptive bayesian wavelet shrinkage’, Journal of the American Statistical Association (92), 14131421. DOI: https://doi.org/10.1080/01621459.1997.10473662

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

Donoho, D. L. (1995a), ‘De-noising by soft-thresholding’, IEEE Transactions on Information Theory (41), 613627. DOI: https://doi.org/10.1109/18.382009

Donoho, D. L. (1995b), ‘Nonlinear solution of linear inverse problems by waveletvaguelette decomposition’, Applied and Computational Harmonic Analysis (2), 10126. DOI: https://doi.org/10.1006/acha.1995.1008

Donoho, D. L. & Johnstone, I. M. (1994a), ‘Ideal denoising in an orthonormal basis chosen from a library of bases’, Comptes Rendus de l Académie des Sciences (319), 13171322.

Donoho, D. L. & Johnstone, I. M. (1994b), ‘Ideal spatial adaptation by wavelet shrinkage’, Biometrika (81), 425455. DOI: https://doi.org/10.1093/biomet/81.3.425

Donoho, D. L. & Johnstone, I. M. (1995), ‘Adapting to unknown smoothness via wavelet shrinkage’, Journal of the American Statistical Association (90), 12001224. DOI: https://doi.org/10.1080/01621459.1995.10476626

Donoho, D. L., Johnstone, I. M., Kerkyacharian, G. & Picard, D. (1995), ‘Wavelet shrinkage: Asymptopia? (with discussion)’, Royal Statistical Society B (57), 301369. DOI: https://doi.org/10.1111/j.2517-6161.1995.tb02032.x

Donoho, D. L., Johnstone, I. M., Kerkyacharian, G. & Picard, D. (1996), ‘Density estimation by wavelet thresholding’, Annals of Statistics 24, 508539. DOI: https://doi.org/10.1214/aos/1032894451

Griffin, J. & Brown, P. (2017), ‘Hierarquical shrinkage priors for regression models’, Bayesian Analysis 12(1), 135–159. DOI: https://doi.org/10.1214/15-BA990

Jansen, M. (2001), Noise reduction by wavelet thresholding, Springer, New York. DOI: https://doi.org/10.1007/978-1-4613-0145-5

Johnstone, L. & Silverman, B. (2005), ‘Empirical bayes selection of wavelet thresholds’, The Annals of Statistics 33, 1700–1752. DOI: https://doi.org/10.1214/009053605000000345

Karagiannis, G., Konomi, B. & Lin, G. (2015), ‘A bayesian mixed shrinkage prior procedure for spatial-stochastic basis selection and evaluation of gpc expansion: applications to elliptic spdes’, Journal of Computational Physics 284, 528–546. DOI: https://doi.org/10.1016/j.jcp.2014.12.034

Lian, H. (2011), ‘On posterior distribution of bayesian wavelet thresholding’, Journal of Statistical Planning and Inference 141, 318–324. DOI: https://doi.org/10.1016/j.jspi.2010.06.016

Mallat, S. G. (1998), A Wavelet Tour of Signal Processing, Academic Press, San Diego. DOI: https://doi.org/10.1016/B978-012466606-1/50008-8

Nason, G. P. (1996), ‘Wavelet shrinkage using cross-validation’, Journal of the Royal Statistical Society B 58, 463479. DOI: https://doi.org/10.1111/j.2517-6161.1996.tb02094.x

Reményi, N. & Vidakovic, B. (2015), ‘Wavelet shrinkage with double weibull prior’, Communications in Statistics: Simulation and Computation 44(1), 88–104. DOI: https://doi.org/10.1080/03610918.2013.765470

Sousa, A. (2020), ‘Bayesian wavelet shrinkage with logistic prior’, Communications in Statistics: Simulation and Computation.

Sousa, A., Garcia, N. & Vidakovic, B. (2021), ‘Bayesian wavelet shrinkage with beta prior’, Computational Statistics 36(2), 1341–1363. DOI: https://doi.org/10.1007/s00180-020-01048-1

Torkamani, R. & Sadeghzadeh, R. (2017), ‘Bayesian compressive sensing using wavelet based markov random fields’, Signal Processing: Image Communication 58, 65–72. DOI: https://doi.org/10.1016/j.image.2017.06.004

Vidakovic, B. (1998), ‘Nonlinear wavelet shrinkage with bayes rules and bayes factors’, Journal of the American Statistical Association 93, 173–179. DOI: https://doi.org/10.1080/01621459.1998.10474099

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

Vidakovic, B. & Ruggeri, F. (2001), ‘Bams method: Theory and simulations’, Sankhya: The Indian Journal of Statistics 63, 234–249.

How to Cite

APA

Santos Sousa, A. R. dos. (2022). Asymmetric Prior in Wavelet Shrinkage. Revista Colombiana de Estadística, 45(1), 41–63. https://doi.org/10.15446/rce.v45n1.92567

ACM

[1]
Santos Sousa, A.R. dos 2022. Asymmetric Prior in Wavelet Shrinkage. Revista Colombiana de Estadística. 45, 1 (Jan. 2022), 41–63. DOI:https://doi.org/10.15446/rce.v45n1.92567.

ACS

(1)
Santos Sousa, A. R. dos. Asymmetric Prior in Wavelet Shrinkage. Rev. colomb. estad. 2022, 45, 41-63.

ABNT

SANTOS SOUSA, A. R. dos. Asymmetric Prior in Wavelet Shrinkage. Revista Colombiana de Estadística, [S. l.], v. 45, n. 1, p. 41–63, 2022. DOI: 10.15446/rce.v45n1.92567. Disponível em: https://revistas.unal.edu.co/index.php/estad/article/view/92567. Acesso em: 19 apr. 2024.

Chicago

Santos Sousa, Alex Rodrigo dos. 2022. “Asymmetric Prior in Wavelet Shrinkage”. Revista Colombiana De Estadística 45 (1):41-63. https://doi.org/10.15446/rce.v45n1.92567.

Harvard

Santos Sousa, A. R. dos (2022) “Asymmetric Prior in Wavelet Shrinkage”, Revista Colombiana de Estadística, 45(1), pp. 41–63. doi: 10.15446/rce.v45n1.92567.

IEEE

[1]
A. R. dos Santos Sousa, “Asymmetric Prior in Wavelet Shrinkage”, Rev. colomb. estad., vol. 45, no. 1, pp. 41–63, Jan. 2022.

MLA

Santos Sousa, A. R. dos. “Asymmetric Prior in Wavelet Shrinkage”. Revista Colombiana de Estadística, vol. 45, no. 1, Jan. 2022, pp. 41-63, doi:10.15446/rce.v45n1.92567.

Turabian

Santos Sousa, Alex Rodrigo dos. “Asymmetric Prior in Wavelet Shrinkage”. Revista Colombiana de Estadística 45, no. 1 (January 19, 2022): 41–63. Accessed April 19, 2024. https://revistas.unal.edu.co/index.php/estad/article/view/92567.

Vancouver

1.
Santos Sousa AR dos. Asymmetric Prior in Wavelet Shrinkage. Rev. colomb. estad. [Internet]. 2022 Jan. 19 [cited 2024 Apr. 19];45(1):41-63. Available from: https://revistas.unal.edu.co/index.php/estad/article/view/92567

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