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Section V: Multivariate and Long Memory Discrete-Valued Processes
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Section V: Multivariate and Long Memory Discrete-Valued Processes
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)
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18: Spatio-Temporal Modeling for Small Area Health Analysis (4/4)
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19: Models for Multivariate Count Time Series (1/4)
Section
V
Multivariate
and
Long
Memory
Discrete-V
alued
Processes
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