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＜電子ブック＞
Wavelets in Functional Data Analysis / by Pedro A. Morettin, Aluísio Pinheiro, Brani Vidakovic
(SpringerBriefs in Mathematics. ISSN:21918201)

版	1st ed. 2017.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2017
大きさ	VIII, 106 p. 44 illus., 25 illus. in color : online resource
著者標目	*Morettin, Pedro A author Pinheiro, Aluísio author Vidakovic, Brani author SpringerLink (Online service)
件　名	LCSH:Functional analysis LCSH:Statistics  LCSH:Mathematical models FREE:Functional Analysis FREE:Statistical Theory and Methods FREE:Mathematical Modeling and Industrial Mathematics
一般注記	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 HTTP:URL=https://doi.org/10.1007/978-3-319-59623-5

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