2: Markov Models for Count Time Series (5/5)
by Richard A. Davis, Scott H. Holan, Robert Lund, Nalini Ravishanker
Handbook of Discrete-Valued Time Series
- Cover
- Half Title
- Series Page
- Title Page
- Copyright Page
- Table of Contents
- Preface
- Editors
- Contributors
- Section I: Methods for Univariate Count Processes
- 1: Statistical Analysis of Count Time Series Models: A GLM Perspective (1/6)
- 1: Statistical Analysis of Count Time Series Models: A GLM Perspective (2/6)
- 1: Statistical Analysis of Count Time Series Models: A GLM Perspective (3/6)
- 1: Statistical Analysis of Count Time Series Models: A GLM Perspective (4/6)
- 1: Statistical Analysis of Count Time Series Models: A GLM Perspective (5/6)
- 1: Statistical Analysis of Count Time Series Models: A GLM Perspective (6/6)
- 2: Markov Models for Count Time Series (1/5)
- 2: Markov Models for Count Time Series (2/5)
- 2: Markov Models for Count Time Series (3/5)
- 2: Markov Models for Count Time Series (4/5)
- 2: Markov Models for Count Time Series (5/5)
- 3: Generalized Linear Autoregressive Moving Average Models (1/6)
- 3: Generalized Linear Autoregressive Moving Average Models (2/6)
- 3: Generalized Linear Autoregressive Moving Average Models (3/6)
- 3: Generalized Linear Autoregressive Moving Average Models (4/6)
- 3: Generalized Linear Autoregressive Moving Average Models (5/6)
- 3: Generalized Linear Autoregressive Moving Average Models (6/6)
- 4: Count Time Series with Observation-Driven Autoregressive Parameter Dynamics (1/5)
- 4: Count Time Series with Observation-Driven Autoregressive Parameter Dynamics (2/5)
- 4: Count Time Series with Observation-Driven Autoregressive Parameter Dynamics (3/5)
- 4: Count Time Series with Observation-Driven Autoregressive Parameter Dynamics (4/5)
- 4: Count Time Series with Observation-Driven Autoregressive Parameter Dynamics (5/5)
- 5: Renewal-Based Count Time Series (1/4)
- 5: Renewal-Based Count Time Series (2/4)
- 5: Renewal-Based Count Time Series (3/4)
- 5: Renewal-Based Count Time Series (4/4)
- 6: State Space Models for Count Time Series (1/5)
- 6: State Space Models for Count Time Series (2/5)
- 6: State Space Models for Count Time Series (3/5)
- 6: State Space Models for Count Time Series (4/5)
- 6: State Space Models for Count Time Series (5/5)
- 7: Estimating Equation Approaches for Integer-Valued Time Series Models (1/4)
- 7: Estimating Equation Approaches for Integer-Valued Time Series Models (2/4)
- 7: Estimating Equation Approaches for Integer-Valued Time Series Models (3/4)
- 7: Estimating Equation Approaches for Integer-Valued Time Series Models (4/4)
- 8: Dynamic Bayesian Models for Discrete-Valued Time Series (1/5)
- 8: Dynamic Bayesian Models for Discrete-Valued Time Series (2/5)
- 8: Dynamic Bayesian Models for Discrete-Valued Time Series (3/5)
- 8: Dynamic Bayesian Models for Discrete-Valued Time Series (4/5)
- 8: Dynamic Bayesian Models for Discrete-Valued Time Series (5/5)
- Section II: Diagnostics and Applications
- 9: Model Validation and Diagnostics (1/6)
- 9: Model Validation and Diagnostics (2/6)
- 9: Model Validation and Diagnostics (3/6)
- 9: Model Validation and Diagnostics (4/6)
- 9: Model Validation and Diagnostics (5/6)
- 9: Model Validation and Diagnostics (6/6)
- 10: Detection of Change Points in Discrete-Valued Time Series (1/6)
- 10: Detection of Change Points in Discrete-Valued Time Series (2/6)
- 10: Detection of Change Points in Discrete-Valued Time Series (3/6)
- 10: Detection of Change Points in Discrete-Valued Time Series (4/6)
- 10: Detection of Change Points in Discrete-Valued Time Series (5/6)
- 10: Detection of Change Points in Discrete-Valued Time Series (6/6)
- 11: Bayesian Modeling of Time Series of Counts with Business Applications (1/4)
- 11: Bayesian Modeling of Time Series of Counts with Business Applications (2/4)
- 11: Bayesian Modeling of Time Series of Counts with Business Applications (3/4)
- 11: Bayesian Modeling of Time Series of Counts with Business Applications (4/4)
- Section III: Binary and Categorical-Valued Time Series
- 12: Hidden Markov Models for Discrete-Valued Time Series (1/4)
- 12: Hidden Markov Models for Discrete-Valued Time Series (2/4)
- 12: Hidden Markov Models for Discrete-Valued Time Series (3/4)
- 12: Hidden Markov Models for Discrete-Valued Time Series (4/4)
- 13: Spectral Analysis of Qualitative Time Series (1/5)
- 13: Spectral Analysis of Qualitative Time Series (2/5)
- 13: Spectral Analysis of Qualitative Time Series (3/5)
- 13: Spectral Analysis of Qualitative Time Series (4/5)
- 13: Spectral Analysis of Qualitative Time Series (5/5)
- 14: Coherence Consideration in Binary Time Series Analysis (1/3)
- 14: Coherence Consideration in Binary Time Series Analysis (2/3)
- 14: Coherence Consideration in Binary Time Series Analysis (3/3)
- Section IV: Discrete-Valued Spatio-Temporal Processes
- 15: Hierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data (1/5)
- 15: Hierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data (2/5)
- 15: Hierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data (3/5)
- 15: Hierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data (4/5)
- 15: Hierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data (5/5)
- 16: Hierarchical Agent-Based Spatio-Temporal Dynamic Models for Discrete-Valued Data (1/4)
- 16: Hierarchical Agent-Based Spatio-Temporal Dynamic Models for Discrete-Valued Data (2/4)
- 16: Hierarchical Agent-Based Spatio-Temporal Dynamic Models for Discrete-Valued Data (3/4)
- 16: Hierarchical Agent-Based Spatio-Temporal Dynamic Models for Discrete-Valued Data (4/4)
- 17: Autologistic Regression Models for Spatio-Temporal Binary Data (1/4)
- 17: Autologistic Regression Models for Spatio-Temporal Binary Data (2/4)
- 17: Autologistic Regression Models for Spatio-Temporal Binary Data (3/4)
- 17: Autologistic Regression Models for Spatio-Temporal Binary Data (4/4)
- 18: Spatio-Temporal Modeling for Small Area Health Analysis (1/4)
- 18: Spatio-Temporal Modeling for Small Area Health Analysis (2/4)
- 18: Spatio-Temporal Modeling for Small Area Health Analysis (3/4)
- 18: Spatio-Temporal Modeling for Small Area Health Analysis (4/4)
- Section V: Multivariate and Long Memory Discrete-Valued Processes
- 19: Models for Multivariate Count Time Series (1/4)
- 19: Models for Multivariate Count Time Series (2/4)
- 19: Models for Multivariate Count Time Series (3/4)
- 19: Models for Multivariate Count Time Series (4/4)
- 20: Dynamic Models for Time Series of Counts with a Marketing Application (1/5)
- 20: Dynamic Models for Time Series of Counts with a Marketing Application (2/5)
- 20: Dynamic Models for Time Series of Counts with a Marketing Application (3/5)
- 20: Dynamic Models for Time Series of Counts with a Marketing Application (4/5)
- 20: Dynamic Models for Time Series of Counts with a Marketing Application (5/5)
- 21: Long Memory Discrete-Valued Time Series (1/3)
- 21: Long Memory Discrete-Valued Time Series (2/3)
- 21: Long Memory Discrete-Valued Time Series (3/3)
- Index (1/2)
- Index (2/2)
49 Markov Models for Count Time Series
Zhu, R. and Joe, H. (2006). Modelling count data time series with markov processes based on binomial
thinning. Journal of Time Series Analysis, 27(5):725–738.
Zhu, R. and Joe, H. (2010a). Count data time series models based on expectation thinning. Stochastic
Models, 26(3):431–462.
Zhu, R. and Joe, H. (2010b). Negative binomial time series models based on expectation thinning
operators. Journal of Statistical Planning and Inference, 140(7):1874–1888.
-
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.