Authors:
- Offers an accessible text to those with little or no exposure to differential equations as modeling objects
- Updates and builds on techniques from the popular Functional Data Analysis (Ramsay and Silverman, 2005)
- Opens up new opportunities for dynamical systems and presents additional applications for previously analyzed data
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Series in Statistics (SSS)
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Table of contents (10 chapters)
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Front Matter
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Back Matter
About this book
Reviews
Authors and Affiliations
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Ottawa, Canada
James Ramsay
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Computational Biology, Cornell University Dept. Biological Statistics &, Ithaca, USA
Giles Hooker
About the authors
Giles Hooker, PhD, is Associate Professor of Biological Statistics and Computational Biology at Cornell University. In addition to differential equation models, he has published extensively on functional data analysis and uncertainty quantification in machine learning. Much of his methodological work is inspired by collaborations in ecology and citizen science data.
Bibliographic Information
Book Title: Dynamic Data Analysis
Book Subtitle: Modeling Data with Differential Equations
Authors: James Ramsay, Giles Hooker
Series Title: Springer Series in Statistics
DOI: https://doi.org/10.1007/978-1-4939-7190-9
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Science+Business Media LLC 2017
Hardcover ISBN: 978-1-4939-7188-6
Softcover ISBN: 978-1-4939-8412-1
eBook ISBN: 978-1-4939-7190-9
Series ISSN: 0172-7397
Series E-ISSN: 2197-568X
Edition Number: 1
Number of Pages: XVII, 230
Number of Illustrations: 34 b/w illustrations, 50 illustrations in colour
Topics: Statistical Theory and Methods, Applications of Mathematics, Big Data/Analytics, Functional Analysis