Wavelet thresholding via a Bayesian approach Export

@article{abramovich98, abstract = {We discuss a Bayesian formalism which gives rise to a type of wavelet threshold estimation in non-parametric regression. A prior distribution is imposed on the wavelet coe\#cients of the unknown response function, designed to capture the sparseness of wavelet expansion common to most applications. For the prior speci\#ed, the posterior median yields a thresholding procedure. Our prior model for the underlying function can be adjusted to give functions falling in any speci\#c Besov space. We ...}, author = {Abramovich, Felix and Sapatinas, T. and Silverman, Bernard}, citeulike-article-id = {1639474}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.7222}, journal = {J. R. Stat. Soc. Ser. B Stat. Methodol.}, keywords = {shrinkage, wavelet}, pages = {725--749}, posted-at = {2007-09-09 20:06:19}, priority = {2}, title = {Wavelet thresholding via a {B}ayesian approach}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.7222}, volume = {60}, year = {1998} }

TY - JOUR ID - abramovich98 L3 - citeulike-article-id:1639474 N2 - We discuss a Bayesian formalism which gives rise to a type of wavelet threshold estimation in non-parametric regression. A prior distribution is imposed on the wavelet coe#cients of the unknown response function, designed to capture the sparseness of wavelet expansion common to most applications. For the prior speci#ed, the posterior median yields a thresholding procedure. Our prior model for the underlying function can be adjusted to give functions falling in any speci#c Besov space. We ... JF - J. R. Stat. Soc. Ser. B Stat. Methodol. EP - 749 TI - Wavelet thresholding via a {B}ayesian approach VL - 60 SP - 725 KW - shrinkage KW - wavelet AU - Abramovich, Felix AU - Sapatinas, T AU - Silverman, Bernard PY - 1998/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.7222 ER -