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Statistics - Statistical Theory and Methods | Non-Linear Time Series - Extreme Events and Integer Value Problems

Non-Linear Time Series

Extreme Events and Integer Value Problems

Turkman, Kamil, Scotto, Manuel González, de Zea Bermudez, Patrícia

2014, XII, 245 p. 41 illus.

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  • Provides an overview of extreme events in time series analysis
  • Discusses recent advances on models for integer valued time series based on thinning operations
  • Describes basic models for nonlinear time series and methods of parameter estimation
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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.

Content Level » Graduate

Keywords » Extreme value theory - Integer valued time series - Non-linear time series

Related subjects » Econometrics / Statistics - Mathematics - Statistical Theory and Methods

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