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 Steins Paradox) make this a highly valuable resource for graduate students and experienced researchers alike.
Preface.- Introduction Examples of Functional Data.- Wavelets.- Wavelet Shrinkage.- Wavelet-based Andrews Plots.- Functional ANOVA.- Further topics. This book is short and offers quick reference on common techniques for application of wavelets on functional data analysis using some real data examples. The authors have provided code examples in Matlab for some of the methods discussed in this book. & this is a useful book for quick reference for researchers in this field. (Abhirup Mallik, Technometrics, Vol. 60 (3), 2018)
Pedro A. Morettin holds a B.S. degree in Mathematics from the University of S?o Paulo, Brazil, with M.A. and Ph.D. degrees in Statistics from the University of California at Berkeley, USA. He is currently emeritus professor at the University of S?o Paulo's Statistics Department. His main research areas include nonparametric statistics, particularly with the use of wavelets and applications to finance. He received the Mahalanobis Award from by the Government of India and the International Statistical Institute in 2009, and the Brazilian Statistical Association Award in 2006.
Alu?sio Pinheiro holds a B.S. and M.S.l³"
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