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Dynamic data analysis : modeling data with differential equations / James Ramsay and Giles Hooker.

By: Contributor(s): Material type: TextTextSeries: Springer series in statisticsPublication details: New York : Springer, 2017.Description: xvii, 230 pages : illustrations (some color) ; 25 cmISBN:
  • 9781493971886 (hbk. : alk. paper)
Subject(s): DDC classification:
  • 000SA.05 23 R179
Contents:
1. Introduction to dynamic models -- 2. Differential equations: notation and architecture -- 3. Linear differential equations and systems -- 4. Nonlinear differential equations and systems -- 5. Numerical solutions -- 6. Qualitative Behavior -- 7. Nonlinear least squares or trajectory matching -- 8. Two-stage least squares or Gradient matching -- 9. Profiled estimation for linear systems estimated by least squared fitting -- 10. Profiled estimation for nonlinear systems.
Summary: 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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Includes bibliographical references and index.

1. Introduction to dynamic models --
2. Differential equations: notation and architecture --
3. Linear differential equations and systems --
4. Nonlinear differential equations and systems --
5. Numerical solutions --
6. Qualitative Behavior --
7. Nonlinear least squares or trajectory matching --
8. Two-stage least squares or Gradient matching --
9. Profiled estimation for linear systems estimated by least squared fitting --
10. Profiled estimation for nonlinear systems.

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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