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The Resource Dynamic Data Analysis : Modeling Data with Differential Equations, by James Ramsay, Giles Hooker, (electronic resource)

Dynamic Data Analysis : Modeling Data with Differential Equations, by James Ramsay, Giles Hooker, (electronic resource)

Creator
Author
Contributor
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. 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 Jim Ramsay, PhD, is Professor Emeritus of Psychology and an Associate Member in the Department of Mathematics and Statistics at McGill University. He received his PhD from Princeton University in 1966 in quantitative psychology. He has been President of the Psychometric Society and the Statistical Society of Canada. He received the Gold Medal in 1998 for his contributions to psychometrics and functional data analysis and Honorary Membership in 2012 from the Statistical Society of Canada. 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.--
Language
eng
Work
Publication
Extent
1 online resource (XVII, 230 pages)
Contents
  • 1 Introduction to Dynamic Models
  • 2 DE notation and types
  • 3 Linear Differential Equations and Systems
  • 4 Nonlinear Differential Equations
  • 5 Numerical Solutions
  • 6 Qualitative Behavior
  • 7 Trajectory Matching
  • 8 Gradient Matching
  • 9 Profiling for Linear Systems
  • 10 Nonlinear Profiling
  • References
  • Glossary
  • Index
Isbn
9781493971909
Link
https://eui.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-1-4939-7190-9
Instance
Label
Dynamic Data Analysis : Modeling Data with Differential Equations
Title
Dynamic Data Analysis
Title remainder
Modeling Data with Differential Equations
Statement of responsibility
by James Ramsay, Giles Hooker
Creator
Contributor
Author
Subject
Language
eng
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. 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 Jim Ramsay, PhD, is Professor Emeritus of Psychology and an Associate Member in the Department of Mathematics and Statistics at McGill University. He received his PhD from Princeton University in 1966 in quantitative psychology. He has been President of the Psychometric Society and the Statistical Society of Canada. He received the Gold Medal in 1998 for his contributions to psychometrics and functional data analysis and Honorary Membership in 2012 from the Statistical Society of Canada. 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.--
Member of
Assigning source
Provided by publisher
http://library.link/vocab/creatorName
Ramsay, James
Image bit depth
0
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
Hooker, Giles.
Series statement
  • Springer eBooks
  • Springer Series in Statistics,
http://library.link/vocab/subjectName
  • Statistics
  • Big data
  • Functional analysis
  • Applied mathematics
  • Engineering mathematics
Label
Dynamic Data Analysis : Modeling Data with Differential Equations, by James Ramsay, Giles Hooker, (electronic resource)
Link
https://eui.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-1-4939-7190-9
Instantiates
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
1 Introduction to Dynamic Models -- 2 DE notation and types -- 3 Linear Differential Equations and Systems -- 4 Nonlinear Differential Equations -- 5 Numerical Solutions -- 6 Qualitative Behavior -- 7 Trajectory Matching -- 8 Gradient Matching -- 9 Profiling for Linear Systems -- 10 Nonlinear Profiling -- References -- Glossary -- Index
Control code
978-1-4939-7190-9
Dimensions
unknown
Extent
1 online resource (XVII, 230 pages)
File format
multiple file formats
Form of item
  • online
  • electronic
Governing access note
Use of this electronic resource may be governed by a license agreement which restricts use to the European University Institute community. Each user is responsible for limiting use to individual, non-commercial purposes, without systematically downloading, distributing, or retaining substantial portions of information, provided that all copyright and other proprietary notices contained on the materials are retained. The use of software, including scripts, agents, or robots, is generally prohibited and may result in the loss of access to these resources for the entire European University Institute community
Isbn
9781493971909
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.1007/978-1-4939-7190-9
Other physical details
84 illustrations, 50 illustrations in color.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(OCoLC)992167467
Label
Dynamic Data Analysis : Modeling Data with Differential Equations, by James Ramsay, Giles Hooker, (electronic resource)
Link
https://eui.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-1-4939-7190-9
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
1 Introduction to Dynamic Models -- 2 DE notation and types -- 3 Linear Differential Equations and Systems -- 4 Nonlinear Differential Equations -- 5 Numerical Solutions -- 6 Qualitative Behavior -- 7 Trajectory Matching -- 8 Gradient Matching -- 9 Profiling for Linear Systems -- 10 Nonlinear Profiling -- References -- Glossary -- Index
Control code
978-1-4939-7190-9
Dimensions
unknown
Extent
1 online resource (XVII, 230 pages)
File format
multiple file formats
Form of item
  • online
  • electronic
Governing access note
Use of this electronic resource may be governed by a license agreement which restricts use to the European University Institute community. Each user is responsible for limiting use to individual, non-commercial purposes, without systematically downloading, distributing, or retaining substantial portions of information, provided that all copyright and other proprietary notices contained on the materials are retained. The use of software, including scripts, agents, or robots, is generally prohibited and may result in the loss of access to these resources for the entire European University Institute community
Isbn
9781493971909
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.1007/978-1-4939-7190-9
Other physical details
84 illustrations, 50 illustrations in color.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(OCoLC)992167467

Subject

Member of

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