Handbook of discrete-valued time series

Content: Front Cover
 Contents
 Preface
 Editors
 Contributors
 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 SeriesSection 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 ProcessesChapter 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 ApplicationChapter 21: Long Memory Discrete-Valued Time Series
 Back Cover.