9783319769370 - stochastic models for time series: 80 di doukhan, paul (18 risultati)
Lingua: Inglese
Editore: Springer, 2018
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Lingua: Inglese
Editore: Springer, 2018
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Editore: Springer, 2018
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Editore: Springer 2018-05, 2018
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Editore: Springer, 2018
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Lingua: Inglese
Editore: Springer, 2018
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Editore: Springer Verlag, 2018
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Paperback. Condizione: Brand New. 332 pages. 9.25x6.10x0.79 inches. In Stock.
Lingua: Inglese
Editore: Springer, 2018
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Da: preigu, Osnabrück, Germaniapreigu
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Taschenbuch. Condizione: Neu. Stochastic Models for Time Series | Paul Doukhan | Taschenbuch | Mathématiques et Applications | xx | Englisch | 2018 | Springer | EAN 9783319769370 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: pr…eigu.
Lingua: Inglese
Editore: Springer, 2018
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 proposesa 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 shifts time series models.Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit theorems) are described under SRD; mixing and weak dependence are also reviewed. In closing, it describes moment techniques together with their relations to cumulant sums as well as an application to kernel type estimation.The appendix reviews basic probability theory facts and discusses useful laws stemming from the Gaussian laws as well as the basic principles of probability, and is completed by R-scripts used for the figures.Richly illustrated with examples and simulations, the book is recommended for advanced master courses for mathematicians just entering the field of time series, and statisticians who want more mathematical insights into the background of non-linear time series.
Lingua: Inglese
Editore: Springer, 2018
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Da: Mispah books, Redhill, SURRE, Regno UnitoMispah books
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Paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Lingua: Inglese
Editore: Springer, 2018
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Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer International Publishing Mai 2018, 2018
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 ran…ge 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 proposesa 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 shifts time series models.Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit theorems) are described under SRD; mixing and weak dependence are also reviewed. In closing, it describes moment techniques together with their relations to cumulant sums as well as an application to kernel type estimation.The appendix reviews basic probability theory facts and discusses useful laws stemming from the Gaussian laws as well as the basic principles of probability, and is completed by R-scripts used for the figures.Richly illustrated with examples and simulations, the book is recommended for advanced master courses for mathematicians just entering the field of time series, and statisticians who want more mathematical insights into the background of non-linear time series. 332 pp. Englisch.
Lingua: Inglese
Editore: Springer International Publishing, 2018
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Da: moluna, Greven, Germaniamoluna
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Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers mathematically oriented statisticians tools for studying non-linear time-seriesDiscusses moment based techniques  Richly illustrated with examples and simulationsProvides material…for mathematicians entering the field of .
Lingua: Inglese
Editore: Springer, 2018
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Condizione: New. Print on Demand.
Lingua: Inglese
Editore: Springer, 2018
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Condizione: New. PRINT ON DEMAND.
Lingua: Inglese
Editore: Springer, Palgrave Macmillan Mai 2018, 2018
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 o…f 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 shifts time series models. Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit theorems) are described under SRD; mixing and weak dependence are also reviewed. In closing, it describes moment techniques together with their relations to cumulant sums as well as an application to kernel type estimation.The appendix reviews basic probability theory facts and discusses useful laws stemming from the Gaussian laws as well as the basic principles of probability, and is completed by R-scripts used for the figures. Richly illustrated with examples and simulations, the book is recommended for advanced master courses for mathematicians just entering the field of time series, and statisticians who want more mathematical insights into the background of non-linear time series.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 332 pp. Englisch.
