书目名称 | Wavelets in Functional Data Analysis |
编辑 | Pedro A. Morettin,Aluísio Pinheiro,Brani Vidakovic |
视频video | |
概述 | Brings together results in wavelet functional data analysis that to date were only available in papers.The only book to present functional data analysis from a wavelet point of view in a general frame |
丛书名称 | SpringerBriefs in Mathematics |
图书封面 |  |
描述 | 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.. |
出版日期 | Book 2017 |
关键词 | Nonparametric statistics; shrinkage; multiresolution analysis; high dimension hypotheses testing; functi |
版次 | 1 |
doi | https://doi.org/10.1007/978-3-319-59623-5 |
isbn_softcover | 978-3-319-59622-8 |
isbn_ebook | 978-3-319-59623-5Series ISSN 2191-8198 Series E-ISSN 2191-8201 |
issn_series | 2191-8198 |
copyright | The Author(s) 2017 |