Dynamic Data Analysis Modeling Data with Differential Equations

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Synopsis

This text focuses on the use of smoothing methods for developing and estimating differential equations following recent developments in functional data analysis and building on techniques described in Ramsay and Silverman (2005) Functional Data Analysis. The central concept of a dynamical system as a buffer that translates sudden changes in input into smooth controlled output responses has led to applications of previously analyzed data, opening up entirely new opportunities for dynamical systems. The technical level has been kept low so that those with little or no exposure to differential equations as modeling objects can be brought into this data analysis landscape. There are already many texts on the mathematical properties of ordinary differential equations, or dynamic models, and there is a large literature distributed over many fields on models for real world processes consisting of differential equations. However, a researcher interested in fitting such a model to data, or a statistician interested in the properties of differential equations estimated from data will find rather less to work with. This book fills that gap.

Book details

Series:
Springer Series in Statistics
Author:
James Ramsay, Giles Hooker
ISBN:
9781493971909
Related ISBNs:
9781493971886
Publisher:
Springer New York
Pages:
N/A
Reading age:
Not specified
Includes images:
Yes
Date of addition:
2018-09-27
Usage restrictions:
Copyright
Copyright date:
2017
Copyright by:
Springer New York, New York, NY 
Adult content:
No
Language:
English
Categories:
Business and Finance, Computers and Internet, Mathematics and Statistics, Nonfiction