Wavelet methods in statistics: Some recent developments and their applications
A Antoniadis - 2007 - projecteuclid.org
The development of wavelet theory has in recent years spawned applications in signal
processing, in fast algorithms for integral transforms, and in image and function …
processing, in fast algorithms for integral transforms, and in image and function …
Modern statistical estimation via oracle inequalities
EJ Candes - Acta numerica, 2006 - cambridge.org
A number of fundamental results in modern statistical theory involve thresholding estimators.
This survey paper aims at reconstructing the history of how thresholding rules came to be …
This survey paper aims at reconstructing the history of how thresholding rules came to be …
Sparse modeling for image and vision processing
In recent years, a large amount of multi-disciplinary research has been conducted on sparse
models and their applications. In statistics and machine learning, the sparsity principle is …
models and their applications. In statistics and machine learning, the sparsity principle is …
[图书][B] Sparse image and signal processing: wavelets, curvelets, morphological diversity
This book presents the state of the art in sparse and multiscale image and signal processing,
covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and …
covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and …
[图书][B] Wavelet methods in statistics with R
GP Nason - 2008 - Springer
The word 'multiscale'can mean many things. However, in this book we are generally
concerned with the representation of objects at a set of scales and then manipulating these …
concerned with the representation of objects at a set of scales and then manipulating these …
Hybrid seismic denoising using higher‐order statistics and improved wavelet block thresholding
SM Mousavi, CA Langston - Bulletin of the Seismological …, 2016 - pubs.geoscienceworld.org
We introduce a nondiagonal seismic denoising method based on the continuous wavelet
transform with hybrid block thresholding (BT). Parameters for the BT step are adaptively …
transform with hybrid block thresholding (BT). Parameters for the BT step are adaptively …
Incorporating information on neighbouring coefficients into wavelet estimation
TT Cai, BW Silverman - Sankhyā: The Indian Journal of Statistics, Series B, 2001 - JSTOR
In standard wavelet methods, the empirical wavelet coefficients are thresholded term by
term, on the basis of their individual magnitudes. Information on other coefficients has no …
term, on the basis of their individual magnitudes. Information on other coefficients has no …
Wavelet estimators in nonparametric regression: a comparative simulation study
A Antoniadis, J Bigot, T Sapatinas - Journal of statistical software, 2001 - jstatsoft.org
Wavelet analysis has been found to be a powerful tool for the nonparametric estimation of
spatially-variable objects. We discuss in detail wavelet methods in nonparametric …
spatially-variable objects. We discuss in detail wavelet methods in nonparametric …
Adaptive wavelet estimation: a block thresholding and oracle inequality approach
TT Cai - The Annals of statistics, 1999 - projecteuclid.org
We study wavelet function estimation via the approach of block thresholding and ideal
adaptation with oracle. Oracle inequalities are derived and serve as guides for the selection …
adaptation with oracle. Oracle inequalities are derived and serve as guides for the selection …