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Handbook of discrete-valued time series / [edited by] Richard A. Davis...[et al.].

Contributor(s): Davis, Richard A [editor] | Holan, Scott H [editor] | Lund, Robert [editor] | Ravishanker, Nalini [editor].
Material type: TextTextSeries: Chapman & Hall/CRC handbooks of modern statistical methods.Publisher: Boca Raton : CRC Press, ©2016Description: xix, 464 pages : illustrations ; 27 cm.ISBN: 9781466577732 .Subject(s): Time-series analysis | Discrete-time systems -- Mathematical models | Linear systems -- Mathematical modelsDDC classification: 000SA.3
Contents:
Section I: Methods for Univariate Count Processes; Chapter 1: Statistical Analysis of Count Time Series Models: A GLM Perspective; Chapter 2: Markov Models for Count Time Series; Chapter 3: Generalized Linear Autoregressive Moving Average Models; Chapter 4: Count Time Series with Observation-Driven Autoregressive Parameter Dynamics; Chapter 5: Renewal-Based Count Time Series; Chapter 6: State Space Models for Count Time Series; Chapter 7: Estimating Equation Approaches for Integer-Valued Time Series Models. Chapter 8: Dynamic Bayesian Models for Discrete-Valued Time Series; Section II: Diagnostics and Applications; Chapter 9: Model Validation and Diagnostics; Chapter 10: Detection of Change Points in Discrete-Valued Time Series; Chapter 11: Bayesian Modeling of Time Series of Counts with Business Applications; Section III: Binary and Categorical-Valued Time Series; Chapter 12: Hidden Markov Models for Discrete-Valued Time Series; Chapter 13: Spectral Analysis of Qualitative Time Series; Chapter 14: Coherence Consideration in Binary Time Series Analysis. Section IV: Discrete-Valued Spatio-Temporal Processes Chapter 15: Hierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data; Chapter 16: Hierarchical Agent-Based Spatio-Temporal Dynamic Models for Discrete-Valued Data; Chapter 17: Autologistic Regression Models for Spatio-Temporal Binary Data; Chapter 18: Spatio-Temporal Modeling for Small Area Health Analysis; Section V: Multivariate and Long Memory Discrete-Valued Processes; Chapter 19: Models for Multivariate Count Time Series. Chapter 20: Dynamic Models for Time Series of Counts with a Marketing Application; Chapter 21: Long Memory Discrete-Valued Time Series.
Summary: 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.
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Item type Current location Call number Status Date due Barcode Item holds
Books Books ISI Library, Kolkata
 
000SA.3 D263 (Browse shelf) Available 137463
Total holds: 0

Includes bibliographical references and index.

Section I: Methods for Univariate Count Processes;
Chapter 1: Statistical Analysis of Count Time Series Models: A GLM Perspective;
Chapter 2: Markov Models for Count Time Series;
Chapter 3: Generalized Linear Autoregressive Moving Average Models;
Chapter 4: Count Time Series with Observation-Driven Autoregressive Parameter Dynamics;
Chapter 5: Renewal-Based Count Time Series;
Chapter 6: State Space Models for Count Time Series;
Chapter 7: Estimating Equation Approaches for Integer-Valued Time Series Models.
Chapter 8: Dynamic Bayesian Models for Discrete-Valued Time Series;
Section II: Diagnostics and Applications;
Chapter 9: Model Validation and Diagnostics;
Chapter 10: Detection of Change Points in Discrete-Valued Time Series;
Chapter 11: Bayesian Modeling of Time Series of Counts with Business Applications;
Section III: Binary and Categorical-Valued Time Series;
Chapter 12: Hidden Markov Models for Discrete-Valued Time Series;
Chapter 13: Spectral Analysis of Qualitative Time Series; Chapter 14: Coherence Consideration in Binary Time Series Analysis.
Section IV: Discrete-Valued Spatio-Temporal Processes
Chapter 15: Hierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data;
Chapter 16: Hierarchical Agent-Based Spatio-Temporal Dynamic Models for Discrete-Valued Data;
Chapter 17: Autologistic Regression Models for Spatio-Temporal Binary Data;
Chapter 18: Spatio-Temporal Modeling for Small Area Health Analysis; Section V: Multivariate and Long Memory Discrete-Valued Processes;
Chapter 19: Models for Multivariate Count Time Series.
Chapter 20: Dynamic Models for Time Series of Counts with a Marketing Application;
Chapter 21: Long Memory Discrete-Valued Time Series.

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.

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