Wavelets and Statistics: Lecture Notes in Statistics, cartea 103
Autor Anestis Antoniadis, Georges Oppenheimen Limba Engleză Paperback – 27 iul 1995
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Specificații
ISBN-13: 9780387945644
ISBN-10: 0387945644
Pagini: 420
Ilustrații: VI, 414 p. 27 illus.
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.63 kg
Editura: Springer
Colecția Lecture Notes in Statistics
Seria Lecture Notes in Statistics
Locul publicării:New York, NY, United States
ISBN-10: 0387945644
Pagini: 420
Ilustrații: VI, 414 p. 27 illus.
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.63 kg
Editura: Springer
Colecția Lecture Notes in Statistics
Seria Lecture Notes in Statistics
Locul publicării:New York, NY, United States
Public țintă
ResearchCuprins
Thresholding of Wavelet Coefficients as Multiple Hypotheses Testing Procedure.- Wavelets, spectrum analysis and 1/f processes.- Variance Function Estimation in Regression by Wavelet Methods.- Locally Self Similar Gaussian Processes.- WaveLab and Reproducible Research.- Extrema Reconstructions and Spline Smoothing: Variations on an Algorithm of Mallat & Zhong..- Identification of Chirps with Continuous Wavelet Transform.- Nonlinear Approximation of Stochastic Processes.- Translation-Invariant De-Noising.- Estimating Wavelet Coefficients.- Nonparametric Supervised Image Segmentation by Energy Minimization using Wavelets.- On the Statistics of Best Bases Criteria.- Discretized Wavelet Density Estimators for Continuous Time Stochastic Processes.- Wavelets and Markov Random Fields in a Bayesian Framework.- Micronde : a Matlab Wavelet Toolbox for Signals and Images.- Wavelet Function Estimation using Cross-Validation.- The Stationary Wavelet Transform and some Statistical Applications.- Wavelet Thresholding: Beyond the Gaussian I.I.D. Situation.- L2(0,1) Weak Convergence of the Empirical Process for Dependent Variables.- Top-Down and Bottom-Up Tree Search Algorithms for Selecting Bases in Wavelet Packet Transforms.- WavBox 4: A Software Toolbox for Wavelet Transforms and Adaptive Wavelet Packet Decompositions.- Using Wavelets for Classifying Human in vivo Magnetic Resonance.- Adaptive Density Estimation.- Wavelets and Regression Analysis.- Reader’s Guide.