Wavelets in Functional Data Analysis

Pedro A. Morettin

ISBN 10: 3319596225 / ISBN 13: 9783319596228

Published by Springer, Berlin, Springer International Publishing, Springer Nov 2017, 2017

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 106 pp. Englisch.

Seller Inventory # 9783319596228

Bibliographic Details

• Title: Wavelets in Functional Data Analysis
• Publisher: Springer, Berlin, Springer International Publishing, Springer Nov 2017
• Publication Date: 2017
• Language: English
• ISBN 10: 3319596225
• ISBN 13: 9783319596228
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 236W x 156H x 9D millimeters 236 millimeters width by 156 millimeters height by 9 millimeters depth
• Weight: 200 grams

About this title

Synopsis

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.

About the Author

Pedro A. Morettin holds a B.S. degree in Mathematics from the University of São 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 São 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.

Aluísio Pinheiro holds a B.S. and M.S. in Statistics from National School of Statistical Sciences (ENCE), Brazil, and University of Campinas, respectively. He also has a Ph.D. in Statistics from the University of North Carolina at Chapel Hill, USA. He is currently affiliated to the University of Campinas. His main research areas are nonparametric statistics, estimation and asymptotics, particularly wavelets and U-statistics. In 2012 he was awarded the P. K. Sen Distinguished Visiting Professorship of Biostatistics at the University of North Carolina.

Brani Vidakovic holds a B.S. in Mathematics and a M.S. in Probability from Belgrade University, Serbia, and a Ph.D. in Statistics from Purdue University, USA (1992). He is currently affiliated to Georgia Tech and Emory University, both in the USA. His main research areas are Bayesian modeling, wavelet statistics and multi-scale data analysis. He was the recipient of the 1992 Burr's award for best Ph.D. student at Purdue University. He is an associate editor of several leading statistical journals.