Wavelets in functional data analysis
著者
書誌事項
Wavelets in functional data analysis
（SpringerBriefs in mathematics, . SBMAC SpringerBriefs）
Springer, c2017
- : [pbk.]
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注記
Includes bibliographical references (p. 99-104) and index
内容説明・目次
内容説明
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.
目次
Preface.- Introduction Examples of Functional Data.- Wavelets.- Wavelet Shrinkage.- Wavelet-based Andrews Plots.- Functional ANOVA.- Further topics.
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