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| Softcover ISBN: | 978-0-8218-4185-3 |
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| eBook ISBN: | 978-1-4704-2979-9 |
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| Softcover ISBN: | 978-0-8218-4185-3 |
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Book DetailsFields Institute CommunicationsVolume: 11; 1997; 252 ppMSC: Primary 62; 58
This book is a collection of research and expository papers reflecting the interfacing of two fields: nonlinear dynamics (in the physiological and biological sciences) and statistics. It presents the proceedings of a four-day workshop entitled “Nonlinear Dynamics and Time Series: Building a Bridge Between the Natural and Statistical Sciences” held at the Centre de Recherches Mathématiques (CRM) in Montréal in July 1995. The goal of the workshop was to provide an exchange forum and to create a link between two diverse groups with a common interest in the analysis of nonlinear time series data.
The editors and peer reviewers of this work have attempted to minimize the problems of maintaining communication between the different scientific fields. The result is a collection of interrelated papers that highlight current areas of research in statistics that might have particular applicability to nonlinear dynamics and new methodology and open data analysis problems in nonlinear dynamics that might find their way into the toolkits and research interests of statisticians.
Features:
- A survey of state-of-the-art developments in nonlinear dynamics time series analysis with open statistical problems and areas for further research.
- Contributions by statisticians to understanding and improving modern techniques commonly associated with nonlinear time series analysis, such as surrogate data methods and estimation of local Lyapunov exponents.
- Starting point for both scientists and statisticians who want to explore the field.
- Expositions that are readable to scientists outside the featured fields of specialization.
Titles in this series are co-published with the Fields Institute for Research in Mathematical Sciences (Toronto, Ontario, Canada).
ReadershipGraduate students, mathematicians, statisticians, nonlinear dynamicists, physicists, and biologists from all fields who are interested in using nonlinear dynamics techniques to study their time series data.
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Table of Contents
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Chapters
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Henry Abarbanel — Tools for the analysis of chaotic data
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Howell Tong — Some comments on nonlinear time series analysis
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Colleen Cutler — A general approach to predictive and fractal scaling dimensions in discrete-index time series
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Louis Pecora, Thomas Carroll and James Heagy — Statistics for continuity and differentiability: An application to attractor reconstruction from time series
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Timothy Sauer — Reconstruction of integrate-and-fire dynamics
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Kung-sik Chan — On the validity of the method of surrogate data
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James Theiler and Dean Prichard — Using "Surrogate Surrogate Data" to calibrate the actual rate of false positives in tests for nonlinearity in time series
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Barbara Bailey, Stephen Ellner and Douglas Nychka — Chaos with confidence: Asymptotics and applications of local Lyapunov exponents
-
Zhan-Qian Lu and Richard Smith — Estimating local Lyapunov exponents
-
Peter Hall — Defining and measuring long-range dependence
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Peter Robinson and Paolo Zaffaroni — Modelling nonlinearity and long memory in time series
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Mark Berliner, Steven MacEachern and Catherine Forbes — Ergodic distributions of random dynamical systems
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Lisa Borland — Detecting structure in noise
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Martin Casdagli — Characterizing nonlinearity in weather and epilepsy data: A personal view
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Andre Longtin and Daniel Racicot — Assessment of linear and nonlinear correlations between neural firing events
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Stephen Merrill and John Cochran — Markov chain methods in the analysis of heart ratevariability
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Reviews
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An important inter-disciplinary work ... provides a valuable collection of recent research ... should appeal to scientists and statisticians who are relatively new to the field and to others interested in a very readable exploration of the topics covered.
Journal of Computational Intelligence in Finance
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RequestsReview Copy – for publishers of book reviewsAccessibility – to request an alternate format of an AMS title
- Book Details
- Table of Contents
- Reviews
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This book is a collection of research and expository papers reflecting the interfacing of two fields: nonlinear dynamics (in the physiological and biological sciences) and statistics. It presents the proceedings of a four-day workshop entitled “Nonlinear Dynamics and Time Series: Building a Bridge Between the Natural and Statistical Sciences” held at the Centre de Recherches Mathématiques (CRM) in Montréal in July 1995. The goal of the workshop was to provide an exchange forum and to create a link between two diverse groups with a common interest in the analysis of nonlinear time series data.
The editors and peer reviewers of this work have attempted to minimize the problems of maintaining communication between the different scientific fields. The result is a collection of interrelated papers that highlight current areas of research in statistics that might have particular applicability to nonlinear dynamics and new methodology and open data analysis problems in nonlinear dynamics that might find their way into the toolkits and research interests of statisticians.
Features:
- A survey of state-of-the-art developments in nonlinear dynamics time series analysis with open statistical problems and areas for further research.
- Contributions by statisticians to understanding and improving modern techniques commonly associated with nonlinear time series analysis, such as surrogate data methods and estimation of local Lyapunov exponents.
- Starting point for both scientists and statisticians who want to explore the field.
- Expositions that are readable to scientists outside the featured fields of specialization.
Titles in this series are co-published with the Fields Institute for Research in Mathematical Sciences (Toronto, Ontario, Canada).
Graduate students, mathematicians, statisticians, nonlinear dynamicists, physicists, and biologists from all fields who are interested in using nonlinear dynamics techniques to study their time series data.
-
Chapters
-
Henry Abarbanel — Tools for the analysis of chaotic data
-
Howell Tong — Some comments on nonlinear time series analysis
-
Colleen Cutler — A general approach to predictive and fractal scaling dimensions in discrete-index time series
-
Louis Pecora, Thomas Carroll and James Heagy — Statistics for continuity and differentiability: An application to attractor reconstruction from time series
-
Timothy Sauer — Reconstruction of integrate-and-fire dynamics
-
Kung-sik Chan — On the validity of the method of surrogate data
-
James Theiler and Dean Prichard — Using "Surrogate Surrogate Data" to calibrate the actual rate of false positives in tests for nonlinearity in time series
-
Barbara Bailey, Stephen Ellner and Douglas Nychka — Chaos with confidence: Asymptotics and applications of local Lyapunov exponents
-
Zhan-Qian Lu and Richard Smith — Estimating local Lyapunov exponents
-
Peter Hall — Defining and measuring long-range dependence
-
Peter Robinson and Paolo Zaffaroni — Modelling nonlinearity and long memory in time series
-
Mark Berliner, Steven MacEachern and Catherine Forbes — Ergodic distributions of random dynamical systems
-
Lisa Borland — Detecting structure in noise
-
Martin Casdagli — Characterizing nonlinearity in weather and epilepsy data: A personal view
-
Andre Longtin and Daniel Racicot — Assessment of linear and nonlinear correlations between neural firing events
-
Stephen Merrill and John Cochran — Markov chain methods in the analysis of heart ratevariability
-
An important inter-disciplinary work ... provides a valuable collection of recent research ... should appeal to scientists and statisticians who are relatively new to the field and to others interested in a very readable exploration of the topics covered.
Journal of Computational Intelligence in Finance
