Incorporating Information on Neighboring Coefficients Into Wavelet Estimation

By Tommaso Cai, Bernard Walter Silverman. 2001

406 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/69244f0dc21e865ab691b0f0964ba65f46bd9b48

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Citations in the corpus

Real nonparametric regression using complex wavelets (2004, methodology): “block thresholding rules of Cai and Silverman (2001) and hence we omit these methods in”
Adapting to Unknown Smoothness by Aggregation of Thresholded Wavelet Estimators (2006, methodology): “To the authors’s knowledge, the result obtained, for instance, by the hard thresholded estimator (see [21]), by the global wavelet block thresholded estimator (see [37]), by the localized wavelet block thresholded estimator (see [9, 12, 10], [28, 27], [24, 25], [16] and [11]) and, in particular, the”
Empirical Bayes approach to block wavelet function estimation (2002, methodology): “We refer to the papers of Cai (1999) and Cai and Silverman (2001) for more details.”
General maximum likelihood empirical Bayes estimation of normal means (2009, background): “Johnstone and Silverman [24] reported results of an extensive simulation study of 18 threshold estimators, including eight options of their EBThresh, the SURE and adaptive SURE [14], the FDR [1] at three levels, three block threshold methods [7, 8] and the soft and”
Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study (2001, methodology): “Hall, Penev, Kerkyacharian & Pickard (1997), Hall, Kerkyacharian & Pickard (1998, 1999), Cai (1999), Efromovich (1999, 2000) and Cai & Silverman (2001) suggested further modifications of the basic thresholding by considering wavelet block thresholding estimators meaning that the wavelet…”
Block thresholding for density estimation: local and global adaptivity (2005, methodology): “For more information on these estimators and the thresholds used see Cai and Silverman [9] and Chicken [10].”
Variational mode decomposition denoising combined the detrended fluctuation analysis (2016, methodology): “The fundamental principle of wavelet filtering [12,13] is that a thresholding rule is applied to wavelet coefficients, and then the inverse discrete wavelet transform (IDWT) reconstructs the signal from these filtered wavelet coefficients.”
Wavelet Analysis and its Statistical Applications (2000, background): “Both modifications imply better asymptotic properties of the resulting wavelet estimators (see, for example, Donoho and Johnstone (1995, 1998), Hall et al. (1998), Cai and Silverman (1998) and Cai (1999)).”
Bivariate shrinkage with local variance estimation (2002, unknown): “An explicit multivariate shrinkage function for wavelet denoising is also presented in [16].”
Denoising of Hyperspectral Imagery Using Principal Component Analysis and Wavelet Shrinkage (2011, background): “Cai and Silverman [4] proposed a thresholding scheme for signal denoising by taking the immediate neighbor coefficients into account.”
ON BLOCK THRESHOLDING IN WAVELET REGRESSION: ADAPTIVITY, BLOCK SIZE, AND THRESHOLD LEVEL (2002, methodology): “For example, we can use the construction of the NeighBlock estimator in Cai and Silverman (2001) to obtain a new version of ˆ f1,λ1.”