Tong | Non-Linear Time Series ' a Dynamical System Approach ' | Buch | 978-0-19-852300-0 | sack.de

Buch, Englisch, Band 6, 580 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 875 g

Reihe: Oxford Statistical Science Series

Tong

Non-Linear Time Series ' a Dynamical System Approach '


Erscheinungsjahr 1993
ISBN: 978-0-19-852300-0
Verlag: OUP Oxford

Buch, Englisch, Band 6, 580 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 875 g

Reihe: Oxford Statistical Science Series

ISBN: 978-0-19-852300-0
Verlag: OUP Oxford


The analysis of time series data has for many years been a central component of statistical research and practice and the theory of linear time series is now well-established. However, the theory of non-linear time series is still a rapidly developing subject. This book is an introduction to some of these developments and the present state of research.

It is a theme of this book that developments in the study of dynamical systems have motivated many of the advances covered here. Consequently, Professor Tong discusses in some detail the fundamental concepts of dynamical systems theory such as limit cycles, Lyapunov exponents, thresholds, and stability, and demonstrates their role in the analysis of non-linear time series.

The result is a book which provides the first accessible and comprehensive account of these exciting new developments and bridges the gap between linear and chaotic time series analysis. Both statisticians and dynamical system theorists will benefit from this account of the interplay between their subjects and the author has included discussions of many of the outstanding open problems which remain.

As a companion to this book, a microcomputer software package is available for the testing for nonlinearity and the fitting of threshold autoregressive models. For more information, please contact the author.

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Autoren/Hrsg.


Weitere Infos & Material


Preface; Acknowledgement; Introduction; An introduction to dynamical systems; Some non-linear time series models; Probability structure; Statistical aspects; Non-linear least-squares prediction based on non-linear models; Case studies; Appendices; References; Index.



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