Stochastic Models for Time Series
This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statistics that directly apply to the time series context to obtain a wide range of modelling possibilities. Functional estimation and bootstrap are discussed, and stationarity is reviewed. The second part describes a number of tools from Gaussian chaos and proposes a tour of linear time series models. It goes on to address nonlinearity from polynomial or chaotic models for which explicit expansions are available, then turns to Markov and non-Markov linear models and discusses Bernoulli shift…
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Produktdetails
- ISBN: 978-3-319-76938-7
- EAN: 9783319769387
- Produktnummer: 33324530
- Verlag: Springer-Verlag GmbH
- Sprache: Englisch
- Erscheinungsjahr: 2018
- Seitenangabe: 308 S.
- Plattform: PDF
- Masse: 4'403 KB
- Abbildungen: 19 schwarz-weiße und 10 farbige Abbildungen, 13 farbige Tabellen, Bibliographie
- Reihenbandnummer: 80
Über den Autor
Paul Doukhan is a Professor at the University of Cergy-Pontoise, Paris. He is an established researcher in the area of non-linear time series. Chiefly focusing on the dependence of stochastic processes, he has published a large number of methodological research papers and authored several books in this research area.
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