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[null,"1.","Record Nr.",null,"UNINA9910144684203321"]
[null,null,"Autore",null,"Vidakovic Brani <1955->"]
[null,null,"Titolo",null,"Statistical modeling by wavelets [[electronic resource] /] / Brani Vidakovic"]
[null,null,"Pubbl/distr/stampa",null,"New York, : Wiley, 1999"]
[null,null,"ISBN",null,"1-282-30775-4"]
[{"$nulls":4},"9786612307751"]
[{"$nulls":4},"0-470-31702-7"]
[{"$nulls":4},"0-470-31786-8"]
[null,null,"Descrizione fisica",null,"1 online resource (410 p.)"]
[null,null,"Collana",null,"Wiley series in probability and mathematical statistics. Applied probability and statistics section"]
[null,null,"Disciplina",null,"515.2433"]
[{"$nulls":4},"519.5"]
[null,null,"Soggetti",null,"Mathematical statistics"]
[{"$nulls":4},"Wavelets (Mathematics)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","\"A Wiley-Interscience publication.\""]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (p. 345-370) and indexes."]
[null,null,"Nota di contenuto",null,"Statistical Modeling by Wavelets; Contents; Preface; Acknowledgments; 1. Introduction; 1.1. Wavelet Evolution; 1.2. Wavelet Revolution; 1.3. Wavelets and Statistics; 1.4. An Appetizer: California Earthquakes; 2. Prerequisites; 2.1. General; 2.2. Hilben Spaces; 2.2.1. Projection Theorem; 2.2.2. 0rthonomal Sets; 2.2.3. Reproducing Kernel Hilberf Spaces; 2.3. Fourier Transformation; 2.3.1. Basic Properties; 2.3.2. Poisson Summation Formula and Sampling Theorem; 2.3.3. Fourier Series; 2.3.4. Discrete Fourier Transform; 2.4. Heisenberg's Uncertainty Principle; 2.5. Some Important Function Spaces"]
[{"$nulls":4},"2.6. Fundanzentals of Signal Processing2.7. Exercises; 3. Wavelets; 3.1. Continuous Wavelet Transformation; 3.1.1. Basic Properties; 3.1.2. Wavelets for Continuous Transfonnations; 3.2. Discretization of the Continuous Wavelet Transform; 3.3. Multiresolution Analysis; 3.3.1. Derivation of a Wavelet Function; 3.4. Same Important Wavelet Bases; 3.4.1. Haar's Wavelets; 3.4.2. Shannon's Wavelets; 3.4.3. Meyer's Wavelets; 3.4.4. Franklin s Wavelets; 3.4.5. Daubechies ' Conzpactly Supporled Wavelets; 3.5. Some Extensions; 3.5.1. Regularity of Wavelets"]
[{"$nulls":4},"3.5.2. The Least Asytnmetric Daubechies ' Wavelets: Symrnlets3.5.3. Approxintations and Characterizations of Functional Spaces; 3.5.4. Daubechies-Lagarias Algorithm; 3.5.5. Moment Conditions; 3.5.6. "]
[{"$nulls":4},"Interpolating (Cardinal) Wavelets; 3.5.7. Pollen-Type Parameterization of Wavelets; 3.6. Exercises; 4. Discrete Wavelet Transformations; 4.1. Introduction; 4.2. The Cascade Algorithnt; 4.3. The Operator Notation of DWT; 4.3.1. Discrete Wavelet Transfomiations as Linear Transfonnations; 4.4. Exercises; 5. Some Generalizations; 5.1. Coiflets; 5.1.1. Construction of Coifrets"]
[{"$nulls":4},"5.2. Biorthogonal Wavelets5.2.1. Construction of Biorthogonal Wavelets; 5.2.2. B-Spline Wavelets; 5.3. Wavelet Packets; 5.3.1. Basic Properties of Wavelet Packets; 5.3.2. Wavelet Packet Tables; 5.4. Best Basis Selection; 5.4.1. Some Cost Measures and the Best Basis Algorithm; 5.5. ε-Decimated and Stationary Wavelet Transformations; 5.5.1. ε-Decimated Wavelet Transformation; 5.5.2. Stationary (Non-Decimated) Wavelet Transformation; 5.6. Periodic Wavelet Transformations; 5.7. Multivariate Wavelet Transfornations; 5.8. Discussion; 5.9. Exercises; 6. Wavelet Shrinkage; 6.1. Shrinkage Method"]
[{"$nulls":4},"6.2. Lineur Wavelet Regression Estimators6.2.1. Wavelet Kernels; 6.2.2. Local Constant Fit Estimators; 6.3. The Simplest Non-Linear Wavelet Shrinkage: Tliresholding; 6.3.1. Variable Selection and Thresholding; 6.3.2. Oracular Risk for Thresholding Rules; 6.3.3. Why the Wavelet Shrinkage Works; 6.3.4. Almost Sure Convergence of Wavelet Sh rinkuge Est imaf ors; 6.4. General Minimax Paradigm; 6.4.1. Translation of Minimaxity Results to the Wavelet Domain; 6.5. Thresholding Policies and Thresholdkg Rides; 6.5.1. Exact Risk Analysis of Thresholding Rules; 6.5.2. Large Sample Properties"]
[{"$nulls":4},"6.5.3. Some Orher Shrinkage Rules"]
[null,null,null,"Sommario/riassunto","A comprehensive, step-by-step introduction to wavelets in statistics.What are wavelets? What makes them increasingly indispensable in statistical nonparametrics? Why are they suitable for \"\"time-scale\"\" applications? How are they used to solve such problems as denoising, regression, or density estimation? Where can one find up-to-date information on these newly \"\"discovered\"\" mathematical objects? These are some of the questions Brani Vidakovic answers in Statistical Modeling by Wavelets. Providing a much-needed introduction to the latest tools afforded statisticians by wavelet theory,"]
[null,"2.","Record Nr.",null,"UNINA9910149546903321"]
[null,null,"Autore",null,"Fullman Joe"]
[null,null,"Titolo",null,"Woodland Origami"]
[null,null,"Pubbl/distr/stampa",null,"New York, NY : , : Gareth Stevens Publishing LLLP, , 2016"]
[{"$nulls":4},"©2017"]
[null,null,"ISBN",null,"9781482459432"]
[{"$nulls":4},"1482459434"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (34 pages)"]
[null,null,"Collana",null,"Amazing Origami"]
[null,null,"Disciplina",null,"736.982"]
[null,null,"Soggetti",null,"Forest animals in art"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","Foxes, squirrels, and raccoons are just some of the animals found in the woodland habitat. Usually fur-covered, these animals are just as cute when made out of paper. Readers are introduced to key terms and common folds in order to complete several origami projects in this book. Full-color photographs accompany simple step-by-step instructions to guide readers as they create paper bats, mice, and other woodland animals. Rated easy, medium, or hard, the crafts are designed to help both new and seasoned origamists enjoy this ancient art."]