Nonlinear Dynamics and Time Series ：Building a Bridge Between the Natural and Statistical Sciences ( Fields Institute Communications )

Publication subTitle ：Building a Bridge Between the Natural and Statistical Sciences

Publication series ：Fields Institute Communications

Author： Colleen D. Cutler;Daniel T. Kaplan  

Publisher： American Mathematical Society‎

Publication year： 2006

E-ISBN: 9781470429799

P-ISBN(Hardback):  9780821841853

Subject： O1 Mathematics

Keyword： 暂无分类

Language： ENG

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Nonlinear Dynamics and Time Series

Description

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 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.

Chapter

Cover

Title page

Contents

Preface

Tools for the analysis of chaotic data

Some comments on nonlinear time series analysis

A general approach to predictive and fractal scaling dimensions in discrete-index time series

Statistics for continuity and differentiability: An application to attractor reconstruction from time series

Reconstruction of integrate-and-fire dynamics

On the validity of the method of surrogate data

Using "Surrogate Surrogate Data" to calibrate the actual rate of false positives in tests for nonlinearity in time series

Chaos with confidence: Asymptotics and applications of local Lyapunov exponents

Estimating local Lyapunov exponents

Defining and measuring long-range dependence

Modelling nonlinearity and long memory in time series

Ergodic distributions of random dynamical systems

Detecting structure in noise

Characterizing nonlinearity in weather and epilepsy data: A personal view

Assessment of linear and nonlinear correlations between neural firing events

Markov chain methods in the analysis of heart ratevariability

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