Dynamic Data Analysis - Modeling Data with Differential Equations (Hardcover, 1st ed. 2017)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Springer Series in Statistics
Release date:	June 2017
First published:	2017
Authors:	James Ramsay • Giles Hooker
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	230
Edition:	1st ed. 2017
ISBN-13:	978-1-4939-7188-6
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Business & Economics > Business & management > Business mathematics & systems > General Books > Science & Mathematics > Mathematics > Calculus & mathematical analysis > Functional analysis Books > Science & Mathematics > Mathematics > Applied mathematics > General
LSN:	1-4939-7188-3
Barcode:	9781493971886

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