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Asymmetric Prior in Wavelet Shrinkage
Priori asimétrico en contracción de ondículas
DOI:
https://doi.org/10.15446/rce.v45n1.92567Keywords:
Wavelet shrinkage, Nonparametric regression, Asymmetric beta distribution (en)Contracción de las ondículas, Regresión no paramétrico, Distribución beta asimétrica (es)
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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.
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1. Alex Rodrigo dos Santos Sousa. (2024). A Bayesian wavelet shrinkage rule under LINEX loss function. Research in Statistics, 2(1) https://doi.org/10.1080/27684520.2024.2362926.
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