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Wavelets in functional data analysis
~
Morettin, Pedro A.
Wavelets in functional data analysis
Record Type:
Electronic resources : Monograph/item
Title/Author:
Wavelets in functional data analysisby Pedro A. Morettin, Aluisio Pinheiro, Brani Vidakovic.
Author:
Morettin, Pedro A.
other author:
Pinheiro, Aluisio.
Published:
Cham :Springer International Publishing :2017.
Description:
viii, 106 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
Subject:
Wavelets (Mathematics)
Online resource:
http://dx.doi.org/10.1007/978-3-319-59623-5
ISBN:
9783319596235$q(electronic bk.)
Wavelets in functional data analysis
Morettin, Pedro A.
Wavelets in functional data analysis
[electronic resource] /by Pedro A. Morettin, Aluisio Pinheiro, Brani Vidakovic. - Cham :Springer International Publishing :2017. - viii, 106 p. :ill., digital ;24 cm. - Springerbriefs in mathematics,2191-8198. - Springerbriefs in mathematics..
Preface -- Introduction Examples of Functional Data -- Wavelets -- Wavelet Shrinkage -- Wavelet-based Andrews Plots -- Functional ANOVA -- Further topics.
Wavelet-based procedures are key in many areas of statistics, applied mathematics, engineering, and science. This book presents wavelets in functional data analysis, offering a glimpse of problems in which they can be applied, including tumor analysis, functional magnetic resonance and meteorological data. Starting with the Haar wavelet, the authors explore myriad families of wavelets and how they can be used. High-dimensional data visualization (using Andrews' plots), wavelet shrinkage (a simple, yet powerful, procedure for nonparametric models) and a selection of estimation and testing techniques (including a discussion on Stein's Paradox) make this a highly valuable resource for graduate students and experienced researchers alike.
ISBN: 9783319596235$q(electronic bk.)
Standard No.: 10.1007/978-3-319-59623-5doiSubjects--Topical Terms:
182734
Wavelets (Mathematics)
LC Class. No.: QA403.3
Dewey Class. No.: 515.2433
Wavelets in functional data analysis
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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.
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Mathematics and Statistics (Springer-11649)
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EB QA403.3 .M845 2017 2017
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1 records • Pages 1 •
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http://dx.doi.org/10.1007/978-3-319-59623-5
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