Non-Linear Time Series

Extreme Events and Integer Value Problems

Business & Finance, Economics, Econometrics, Nonfiction, Science & Nature, Mathematics, Statistics
Cover of the book Non-Linear Time Series by Manuel González Scotto, Patrícia de Zea Bermudez, Kamil Feridun Turkman, Springer International Publishing
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Author: Manuel González Scotto, Patrícia de Zea Bermudez, Kamil Feridun Turkman ISBN: 9783319070285
Publisher: Springer International Publishing Publication: September 29, 2014
Imprint: Springer Language: English
Author: Manuel González Scotto, Patrícia de Zea Bermudez, Kamil Feridun Turkman
ISBN: 9783319070285
Publisher: Springer International Publishing
Publication: September 29, 2014
Imprint: Springer
Language: English

This book offers a useful combination of probabilistic and statistical tools for analyzing nonlinear time series. Key features of the book include a study of the extremal behavior of nonlinear time series and a comprehensive list of nonlinear models that address different aspects of nonlinearity. Several inferential methods, including quasi likelihood methods, sequential Markov Chain Monte Carlo Methods and particle filters, are also included so as to provide an overall view of the available tools for parameter estimation for nonlinear models. A chapter on integer time series models based on several thinning operations, which brings together all recent advances made in this area, is also included.

Readers should have attended a prior course on linear time series, and a good grasp of simulation-based inferential methods is recommended. This book offers a valuable resource for second-year graduate students and researchers in statistics and other scientific areas who need a basic understanding of nonlinear time series.

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This book offers a useful combination of probabilistic and statistical tools for analyzing nonlinear time series. Key features of the book include a study of the extremal behavior of nonlinear time series and a comprehensive list of nonlinear models that address different aspects of nonlinearity. Several inferential methods, including quasi likelihood methods, sequential Markov Chain Monte Carlo Methods and particle filters, are also included so as to provide an overall view of the available tools for parameter estimation for nonlinear models. A chapter on integer time series models based on several thinning operations, which brings together all recent advances made in this area, is also included.

Readers should have attended a prior course on linear time series, and a good grasp of simulation-based inferential methods is recommended. This book offers a valuable resource for second-year graduate students and researchers in statistics and other scientific areas who need a basic understanding of nonlinear time series.

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