Dimension estimation and models:
This volume is the first in the new series Nonlinear Time Series and Chaos. The general aim of the series is to provide a bridge between the two communities by inviting prominent researchers in their respective fields to give a systematic account of their chosen topics, starting at the beginning and...
Gespeichert in:
Format: | Elektronisch E-Book |
---|---|
Sprache: | English |
Veröffentlicht: |
Singapore
World Scientific Pub. Co.
c1993
|
Schriftenreihe: | Nonlinear time series and chaos
v. 1 |
Schlagworte: | |
Online-Zugang: | FHN01 URL des Erstveroeffentlichers |
Zusammenfassung: | This volume is the first in the new series Nonlinear Time Series and Chaos. The general aim of the series is to provide a bridge between the two communities by inviting prominent researchers in their respective fields to give a systematic account of their chosen topics, starting at the beginning and ending with the latest state. It is hoped that researchers in both communities will find the topics relevant and thought provoking. In this volume, the first chapter, written by Professor Colleen Cutler, is a comprehensive account of the theory and estimation of fractal dimension, a topic of central importance in dynamical systems, which has recently attracted the attention of the statisticians. As it is natural to study a stochastic dynamical system within the framework of Markov chains, it is therefore relevant to study their limiting behaviour. The second chapter, written by Professor Kung-Sik Chan, reviews some limit theorems of Markov chains and illustrates their relevance to chaos. The next three chapters are concerned with specific models. Briefly, chapter three by Professor Peter Lewis and Dr Bonnie Ray and chapter four by Professor Peter Brockwell generalise the class of self-exciting threshold autoregressive models in different directions. In chapter three, the new and powerful methodology of multivariate adaptive regression splines (MARS) is adapted to time series data. Its versatility is illustrated by reference to the very interesting and complex sea surface temperature data. Chapter four exploits the greater tractability of continuous-time Markov approach to discrete-time data. The approach is particularly relevant to irregularly sampled data. The concluding chapter, by Professor Pham Dinh Tuan, is likely to be the most definitive account of bilinear models in discrete time to date |
Beschreibung: | vii, 223 p. ill |
ISBN: | 9789814317382 |
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spelling | Dimension estimation and models editor, Howell Tong Singapore World Scientific Pub. Co. c1993 vii, 223 p. ill txt rdacontent c rdamedia cr rdacarrier Nonlinear time series and chaos v. 1 This volume is the first in the new series Nonlinear Time Series and Chaos. The general aim of the series is to provide a bridge between the two communities by inviting prominent researchers in their respective fields to give a systematic account of their chosen topics, starting at the beginning and ending with the latest state. It is hoped that researchers in both communities will find the topics relevant and thought provoking. In this volume, the first chapter, written by Professor Colleen Cutler, is a comprehensive account of the theory and estimation of fractal dimension, a topic of central importance in dynamical systems, which has recently attracted the attention of the statisticians. As it is natural to study a stochastic dynamical system within the framework of Markov chains, it is therefore relevant to study their limiting behaviour. The second chapter, written by Professor Kung-Sik Chan, reviews some limit theorems of Markov chains and illustrates their relevance to chaos. The next three chapters are concerned with specific models. Briefly, chapter three by Professor Peter Lewis and Dr Bonnie Ray and chapter four by Professor Peter Brockwell generalise the class of self-exciting threshold autoregressive models in different directions. In chapter three, the new and powerful methodology of multivariate adaptive regression splines (MARS) is adapted to time series data. Its versatility is illustrated by reference to the very interesting and complex sea surface temperature data. Chapter four exploits the greater tractability of continuous-time Markov approach to discrete-time data. The approach is particularly relevant to irregularly sampled data. The concluding chapter, by Professor Pham Dinh Tuan, is likely to be the most definitive account of bilinear models in discrete time to date Time-series analysis Stochastisches dynamisches System (DE-588)4305316-6 gnd rswk-swf 1\p (DE-588)4143413-4 Aufsatzsammlung gnd-content Stochastisches dynamisches System (DE-588)4305316-6 s 2\p DE-604 Tong, Howell Sonstige oth Erscheint auch als Druck-Ausgabe 9789810213534 Erscheint auch als Druck-Ausgabe 9810213530 http://www.worldscientific.com/worldscibooks/10.1142/1986#t=toc Verlag URL des Erstveroeffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
spellingShingle | Dimension estimation and models Time-series analysis Stochastisches dynamisches System (DE-588)4305316-6 gnd |
subject_GND | (DE-588)4305316-6 (DE-588)4143413-4 |
title | Dimension estimation and models |
title_auth | Dimension estimation and models |
title_exact_search | Dimension estimation and models |
title_full | Dimension estimation and models editor, Howell Tong |
title_fullStr | Dimension estimation and models editor, Howell Tong |
title_full_unstemmed | Dimension estimation and models editor, Howell Tong |
title_short | Dimension estimation and models |
title_sort | dimension estimation and models |
topic | Time-series analysis Stochastisches dynamisches System (DE-588)4305316-6 gnd |
topic_facet | Time-series analysis Stochastisches dynamisches System Aufsatzsammlung |
url | http://www.worldscientific.com/worldscibooks/10.1142/1986#t=toc |
work_keys_str_mv | AT tonghowell dimensionestimationandmodels |