Essential Wavelets for Statistical Applications and Data Analysis

By Sujit Kumar Ghosh. Technometrics, 2001

390 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/3703986f8e4d2a503440d63db4af58baf068e69c

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In Computer Vision and Pattern Recognition

Citations in the corpus

Confidence sets for nonparametric wavelet regression (2005, background): “See, for instance, Cai (1999), Cai and Brown (1998), Efromovich (1999), Johnstone and Silverman (2002) and Ogden (1997).”
A survey of MRI-based brain tumor segmentation methods (2014, methodology): “Many de-noising methods for MRI image have been proposed, such as Anisotropic Diffusion Filtering (ADF) [7,8] , wavelets [9,10] , Non-Local Means (NLM) [11-13] , and Independent Component Analysis (ICA) [14,15] .”
A comparison of model-based and regression classification techniques applied to near infrared spectroscopic data in food authentication studies (2007, background): “Ogden([8]) gives a good practical introduction to wavelet analysis.”
Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study (2001, background): “…reviews and descriptions of the various classical and Bayesian wavelet shrinkage and wavelet thresholding estimators are given in the books by Ogden (1997), Vidakovic (1999) and Percival and Walden (2000), in the papers appeared in the edited volume by Müller & Vidakovic (1999), and in the…”
Using unlabelled data to update classification rules with applications in food authenticity studies (2006, methodology): “A good introduction to wavelet decomposition is given in Ogden (1997).”
Bayesian Analysis for Penalized Spline Regression Using WinBUGS (2005, methodology): “Competing approaches to nonparametric modeling include, but are not limited to, smoothing splines (Eubank [9]; Wahba [27]; Green and Silverman [14]), series-based smoothers (Tarter and Lock [25]; Ogden [20]), kernel methods (Wand and Jones [29]; Fan and Gijbels [10]); regression splines (Hastie and ”
Medical Image Registration (2001, methodology): “8Jean-Baptiste Fourier, a French mathematician, discovered that it is possible to decompose any of a large class of functions into component function of only standard trigonometric functions while studying heat conduction in the early 19th century [18].”
Wavelet regression and additive models for irregularly spaced data (2019, neutral): “Popular choices are Daubechies [Daubechies, 1988], Coiﬂets [Daubechies, 1993], Meyer wavelets [Meyer, 1985], and Spline wavelets [Chui, 1992]; for an overview of wavelet families, see Ogden [2012].”
Wavelets and their applications (2007, background): “Ogden [OGD 97] offers a basic presentation thereof.”
Intercomparison of Spatial Forecast Verification Methods (2009, methodology): “In this way the variogram technique is a type of scale-separation method (cf. Nychka 1998; Ogden 1996).”
Generalized feature extraction for structural pattern recognition in time-series data (2001, background): “The wavelet transformation [19][39][68][71] employs a basis containing waveforms that are localized in space and, therefore, is more suitable for approximating time-series data sets containing regional structures.”
Wavelet Analysis and its Statistical Applications (2000, methodology): “The special structure of wavelet bases may be appreciated by considering the generation of an orthonormal wavelet basis for functions g 2 L(2)(R) (the space of square integrable real functions). Following the approach of Daubechies (1992), which is that most often adopted in applications of wavelets”