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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Dynamic Data Analysis: Modeling Data with Differential Equations

Dynamic Data Analysis: Modeling Data with Differential Equations

Ramsay, James
Hooker, Giles

Paperback

Series: Springer Statistics

Business GeneralGeneral MathematicsProbability & Statistics

ISBN10: 1493984128
ISBN13: 9781493984121
Publisher: Springer Nature
Published: Jul 28 2018
Pages: 230
Weight: 0.78
Height: 0.53 Width: 6.14 Depth: 9.21
Language: English
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.

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