Wavelets in Functional Data Analysis

• Κύριοι συγγραφείς: Morettin, Pedro A. (Συγγραφέας), Pinheiro, Aluísio (Συγγραφέας), Vidakovic, Brani (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Σειρά: SpringerBriefs in Mathematics,
• Θέματα: Mathematics.; Functional analysis.; Mathematical models.; Statistics.; Functional Analysis.; Statistical Theory and Methods.; Mathematical Modeling and Industrial Mathematics.
• Φυσική περιγραφή: VIII, 106 p. 44 illus., 25 illus. in color. online resource.
• ISBN: 9783319596235
• ISSN: 2191-8198

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.