Fields Institute Communications 1997; 252 pp; softcover Volume: 11 ISBN10: 0821841858 ISBN13: 9780821841853 List Price: US$96 Individual Members: US$57.60 Institutional Members: US$76.80 Order Code: FIC/11.S
 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 fourday 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 stateoftheart 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 copublished with the Fields Institute for Research in Mathematical Sciences (Toronto, Ontario, Canada). Readership 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. Reviews "An important interdisciplinary 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 Table of Contents Opening lectures  H. I. Abarbanel  Tools for the analysis of chaotic data
 H. Tong  Some comments on nonlinear time series analysis
Embeddings, dimension, and system reconstruction  C. D. Cutler  A general approach to predictive and fractal scaling dimensions in discreteindex time series
 L. M. Pecora, T. L. Carroll, and J. F. Heagy  Statistics for continuity and differentiability: An application to attractor reconstruction from time series
 T. Sauer  Reconstruction of integrateandfire dynamics
Surrogate data methodology  K.S. Chan  On the validity of the method of surrogate data
 J. Theiler and D. Prichard  Using "surrogate surrogate data" to calibrate the actual rate of false positives in tests for nonlinearity in time series
Local Lyapunov exponents  B. A. Bailey, S. Ellner, and D. W. Nychka  Chaos with confidence: Asymptotics and applications of local Lyapunov exponents
 Z.Q. Lu and R. L. Smith  Estimating local Lyapunov exponents
Longrange dependence  P. Hall  Defining and measuring longrange dependence
 P. M. Robinson and P. Zaffaroni  Modelling nonlinearity and long memory in time series
Data analysis and applications  L. M. Berliner, S. N. MacEachern, and C. S. Forbes  Ergodic distributions of random dynamical systems
 L. Borland  Detecting structure in noise
 M. C. Casdagli  Characterizing nonlinearity in weather and epilepsy data: A personal view
 A. Longtin and D. M. Racicot  Assessment of linear and nonlinear correlations between neural firing events
 S. J. Merrill and J. R. Cochran  Markov chain methods in the analysis of heart rate variability
