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Handbook of discrete - valued time series

Contributor(s): Davis, Richard A [Editor] | Holan, Scott H [Editor] | Lund, Robert [Editor] | Ravishanker, Nalini [Editor].
Series: Chapman and Hall Handbooks of modern statistical methods. Publisher: Boca Raton CRC Press 2016Description: xix, 464 p.ISBN: 9781466577732.Subject(s): Time-series analysis | Sequential analysisDDC classification: 519.55 Summary: Handbook of Discrete-Valued Time Series presents state-of-the-art methods for modeling time series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the book focuses on time series of counts, some of the techniques discussed can be applied to other types of discrete-valued time series, such as binary-valued or categorical time series. Explore a Balanced Treatment of Frequentist and Bayesian Perspectives Accessible to graduate-level students who have taken an elementary class in statistical time series analysis, the book begins with the history and current methods for modeling and analyzing univariate count series. It next discusses diagnostics and applications before proceeding to binary and categorical time series. The book then provides a guide to modern methods for discrete-valued spatio-temporal data, illustrating how far modern applications have evolved from their roots. The book ends with a focus on multivariate and long-memory count series. Get Guidance from Masters in the Field Written by a cohesive group of distinguished contributors, this handbook provides a unified account of the diverse techniques available for observation- and parameter-driven models. It covers likelihood and approximate likelihood methods, estimating equations, simulation methods, and a Bayesian approach for model fitting. (https://www.crcpress.com/Handbook-of-Discrete-Valued-Time-Series/Davis-Holan-Lund-Ravishanker/9781466577732)
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Table of Contents:


SECTION I: METHODS FOR UNIVARIATE COUNT PROCESSES


1. Statistical Analysis of Count Time Series Models: AGLM Perspective
Konstantinos Fokianos

2. Markov Models for Count Time Series
Harry Joe

3. Generalized Linear Autoregressive Moving Average Models
William T.M. Dunsmuir

4. Count Time Series with Observation-Driven Autoregressive Parameter Dynamics
Dag Tjøstheim

5. Renewal-Based Count Time Series
Robert Lund and James Livsey

6. State Space Models for Count Time Series
Richard A. Davis and William T.M. Dunsmuir

7. Estimating Equation Approaches for Integer-Valued Time Series Models
Aerambamoorthy Thavaneswaran and Nalini Ravishanker

8. Dynamic Bayesian Models for Discrete-Valued Time Series
Dani Gamerman, Carlos A. Abanto-Valle, Ralph S. Silva, and Thiago G. Martins


SECTION II: DIAGNOSTICS AND APPLICATIONS


9. Model Validation and Diagnostics
Robert C. Jung, Brendan P.M. McCabe, and A.R. Tremayne

10. Detection of Change Points in Discrete-Valued Time Series
Claudia Kirch and Joseph Tadjuidje Kamgaing

11. Bayesian Modeling of Time Series of Counts with Business Applications
Refik Soyer, Tevfik Aktekin, and Bumsoo Kim


SECTION III: Binary and Categorical-Valued Time Series


12. Hidden Markov Models for Discrete-Valued Time Series
Iain L. MacDonald and Walter Zucchini

13. Spectral Analysis of Qualitative Time Series
David Stoffer

14. Coherence Consideration in Binary Time Series Analysis
Benjamin Kedem


SECTION IV: DISCRETE-VALUED SPATIO-TEMPORAL PROCESSES


15. Hierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data
Scott H. Holan and Christopher K. Wikle

16. Hierarchical Agent-Based Spatio-Temporal Dynamic Models for Discrete-Valued Data
Christopher K. Wikle and Mevin B. Hooten

17. Autologistic Regression Models for Spatio-Temporal Binary Data
Jun Zhu and Yanbing Zheng

18. Spatio-Temporal Modeling for Small Area Health Analysis
Andrew B. Lawson and Ana Corberán-Vallet


SECTION V: MULTIVARIATE AND LONG MEMORY DISCRETE-VALUED PROCESSES


19. Models for Multivariate Count Time Series

20. Dynamic Models for Time Series of Counts with a Marketing Application
Nalini Ravishanker, Rajkumar Venkatesan, and Shan Hu

21. Long Memory Discrete-Valued Time Series
Robert Lund, Scott H. Holan, and James Livsey




Handbook of Discrete-Valued Time Series presents state-of-the-art methods for modeling time series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the book focuses on time series of counts, some of the techniques discussed can be applied to other types of discrete-valued time series, such as binary-valued or categorical time series.

Explore a Balanced Treatment of Frequentist and Bayesian Perspectives

Accessible to graduate-level students who have taken an elementary class in statistical time series analysis, the book begins with the history and current methods for modeling and analyzing univariate count series. It next discusses diagnostics and applications before proceeding to binary and categorical time series. The book then provides a guide to modern methods for discrete-valued spatio-temporal data, illustrating how far modern applications have evolved from their roots. The book ends with a focus on multivariate and long-memory count series.

Get Guidance from Masters in the Field

Written by a cohesive group of distinguished contributors, this handbook provides a unified account of the diverse techniques available for observation- and parameter-driven models. It covers likelihood and approximate likelihood methods, estimating equations, simulation methods, and a Bayesian approach for model fitting.


(https://www.crcpress.com/Handbook-of-Discrete-Valued-Time-Series/Davis-Holan-Lund-Ravishanker/9781466577732)

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