Buch, Englisch, 202 Seiten, Format (B × H): 154 mm x 233 mm, Gewicht: 366 g
Reihe: Use R!
ISBN: 978-0-387-98184-0
Verlag: Springer
The book provides an application-oriented overview of functional analysis, with extended and accessible presentations of key concepts such as spline basis functions, data smoothing, curve registration, functional linear models and dynamic systems
Functional data analysis is put to work in a wide a range of applications, so that new problems are likely to find close analogues in this book
The code in R and Matlab in the book has been designed to permit easy modification to adapt to new data structures and research problems
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Naturwissenschaften Biowissenschaften Biowissenschaften
- Sozialwissenschaften Psychologie Psychologie / Allgemeines & Theorie Experimentelle Psychologie
- Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Außenhandel
- Mathematik | Informatik Mathematik Mathematische Analysis Funktionalanalysis
- Mathematik | Informatik Mathematik Mathematische Analysis Harmonische Analysis, Fourier-Mathematik
- Mathematik | Informatik EDV | Informatik Informatik
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenkompression, Dokumentaustauschformate
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Public Health, Gesundheitsmanagement, Gesundheitsökonomie, Gesundheitspolitik
Weitere Infos & Material
to Functional Data Analysis.- Essential Comparisons of the Matlab and R Languages.- How to Specify Basis Systems for Building Functions.- How to Build Functional Data Objects.- Smoothing: Computing Curves from Noisy Data.- Descriptions of Functional Data.- Exploring Variation: Functional Principal and Canonical Components Analysis.- Registration: Aligning Features for Samples of Curves.- Functional Linear Models for Scalar Responses.- Linear Models for Functional Responses.- Functional Models and Dynamics.
