Non-linear time series : extreme events and integer value problems / Kamil Feridun Turkman, Manuel Gonzalez Scotto and Patricia de Zea Bermudez.
Material type:
TextPublication details: New York : Springer, 2014.Description: xii, 245 p. : illustrations ; 24 cmISBN: - 9783319070278
- 000SA.3 23 T939
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 000SA.3 T939 (Browse shelf(Opens below)) | Available | 136303 |
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| 000SA.3 Sh562 Time series analysis and its applications : with R examples / | 000SA.3 T161 Time series analysis : | 000SA.3 T926 Models for dependent time series / | 000SA.3 T939 Non-linear time series : | 000SA.3 W415 Multivariate time series analysis and applications/ | 000SA.3=4 B815 Series cicliche ed osoillanti | 000SA.3=4 L271 (La) Comparaison et la simplification de fonctions discriminantes lineaires |
Includes bibliographical references.
1. Introduction --
2. Nonlinear time series models --
3. Extremes of nonlinear time series --
4. Inference for nonlinear time series models --
5. Models for integer-valued time series--
Data sets--
References.
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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