Titlebook: Wavelets in Functional Data Analysis; Pedro A. Morettin,Aluísio Pinheiro,Brani Vidakovic Book 2017 The Author(s) 2017 Nonparametric statis

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Wavelets,analysis, modeling and forecasting of time series, functional data analysis, data mining and classification, with ranges of application areas in science and engineering. Wavelets owe their initial popularity in statistics to ., a simple and yet powerful procedure efficient for many nonparametric statistical models.

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Wavelets,analysis, modeling and forecasting of time series, functional data analysis, data mining and classification, with ranges of application areas in science and engineering. Wavelets owe their initial popularity in statistics to ., a simple and yet powerful procedure efficient for many nonparametric statistical models.

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Wavelet Shrinkage,l shrinkage estimators for the location parameters. Some references are Donoho and Johnstone?(1994), Donoho et?al.?(1995), Vidakovic?(1999), Antoniadis et?al.?(2001), Pinheiro and Vidakovic?(1997). In this chapter we focus on the simplest, yet most important shrinkage strategy—wavelet thresholding.

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Wavelets in Functional Data Analysis978-3-319-59623-5Series ISSN 2191-8198 Series E-ISSN 2191-8201

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2191-8198 tric 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..978-3-319-59622-8978-3-319-59623-5Series ISSN 2191-8198 Series E-ISSN 2191-8201

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