Wavelets in functional data analysis

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 meteorologica...

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Detalles Bibliográficos
Autores principales: Morettin, Pedro A, Pinheiro, Aluísio, Vidakovic, Brani
Lenguaje:eng
Publicado: Springer 2017
Materias:
Mathematical Physics and Mathematics
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-59623-5
http://cds.cern.ch/record/2296554
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author Morettin, Pedro A
Pinheiro, Aluísio
Vidakovic, Brani
author_facet Morettin, Pedro A
Pinheiro, Aluísio
Vidakovic, Brani
author_sort Morettin, Pedro A
collection CERN
description 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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spelling cern-22965542021-04-21T18:58:56Zdoi:10.1007/978-3-319-59623-5http://cds.cern.ch/record/2296554engMorettin, Pedro APinheiro, AluísioVidakovic, BraniWavelets in functional data analysisMathematical Physics and MathematicsWavelet-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.Springeroai:cds.cern.ch:22965542017
spellingShingle Mathematical Physics and Mathematics
Morettin, Pedro A
Pinheiro, Aluísio
Vidakovic, Brani
Wavelets in functional data analysis
title Wavelets in functional data analysis
title_full Wavelets in functional data analysis
title_fullStr Wavelets in functional data analysis
title_full_unstemmed Wavelets in functional data analysis
title_short Wavelets in functional data analysis
title_sort wavelets in functional data analysis
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-319-59623-5
http://cds.cern.ch/record/2296554
work_keys_str_mv AT morettinpedroa waveletsinfunctionaldataanalysis
AT pinheiroaluisio waveletsinfunctionaldataanalysis
AT vidakovicbrani waveletsinfunctionaldataanalysis

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