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Wavelets in Functional Data Analysis / by Pedro A. Morettin, Aluísio Pinheiro, Brani Vidakovic.

Springer Nature - Springer Mathematics and Statistics eBooks 2017 English International Available online

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Format:
Book
Author/Creator:
Morettin, Pedro A., Author.
Pinheiro, Aluísio., Author.
Vidakovic, Brani., Author.
Series:
SpringerBriefs in Mathematics, 2191-8201
Language:
English
Subjects (All):
Functional analysis.
Statistics.
Mathematical models.
Functional Analysis.
Statistical Theory and Methods.
Mathematical Modeling and Industrial Mathematics.
Local Subjects:
Functional Analysis.
Statistical Theory and Methods.
Mathematical Modeling and Industrial Mathematics.
Physical Description:
1 online resource (VIII, 106 p. 44 illus., 25 illus. in color.)
Edition:
1st ed. 2017.
Place of Publication:
Cham : Springer International Publishing : Imprint: Springer, 2017.
Summary:
Wavelet-based procedures are key in many areas of statistics, applied mathematics, engineering, and science. This book presents wavelets in functional data analysis, offering a glimpse of problems in which they can be applied, including tumor analysis, functional magnetic resonance and meteorological data. Starting with the Haar wavelet, the authors explore myriad families of wavelets and how they can be used. High-dimensional data visualization (using Andrews' plots), wavelet shrinkage (a simple, yet powerful, procedure for nonparametric models) and a selection of estimation and testing techniques (including a discussion on Stein’s Paradox) make this a highly valuable resource for graduate students and experienced researchers alike.
Contents:
Preface
Introduction Examples of Functional Data
Wavelets
Wavelet Shrinkage
Wavelet-based Andrews Plots
Functional ANOVA
Further topics.
Notes:
Includes bibliographical references and index.
ISBN:
3-319-59623-3

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