Wavelets in Functional Data Analysis

  • Pedro A. Morettin
  • Aluísio Pinheiro
  • Brani Vidakovic

Part of the SpringerBriefs in Mathematics book series (BRIEFSMATH)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Pedro A. Morettin, Aluísio Pinheiro, Brani Vidakovic
    Pages 1-10
  3. Pedro A. Morettin, Aluísio Pinheiro, Brani Vidakovic
    Pages 11-35
  4. Pedro A. Morettin, Aluísio Pinheiro, Brani Vidakovic
    Pages 37-49
  5. Pedro A. Morettin, Aluísio Pinheiro, Brani Vidakovic
    Pages 51-70
  6. Pedro A. Morettin, Aluísio Pinheiro, Brani Vidakovic
    Pages 71-88
  7. Pedro A. Morettin, Aluísio Pinheiro, Brani Vidakovic
    Pages 89-97
  8. Back Matter
    Pages 99-106

About this book

Introduction

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.

Keywords

Nonparametric statistics shrinkage multiresolution analysis high dimension hypotheses testing functional statistical modeling 62G05 62G10 62G20 42C40

Authors and affiliations

  • Pedro A. Morettin
    • 1
  • Aluísio Pinheiro
    • 2
  • Brani Vidakovic
    • 3
  1. 1.Department of StatisticsUniversity of São PauloSão PauloBrazil
  2. 2.Department of StatisticsUniversity of CampinasCampinasBrazil
  3. 3.The Wallace H. Coulter Department of Biomedical EngineeringGeorgia Inst Tech & Emory Univ Sch MedAtlantaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-59623-5
  • Copyright Information The Author(s) 2017
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-59622-8
  • Online ISBN 978-3-319-59623-5
  • Series Print ISSN 2191-8198
  • Series Online ISSN 2191-8201
  • About this book