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Handbook of Discrete-Valued Time Series

Author Richard A. Davis , Scott H. Holan , Robert Lund , Nalini Ravishanker
Release Date: 2016/01/01
ISBN: 9781466577749
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Model a Wide Range of Count Time Series 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 ca

Table of Contents

  1. Cover
  2. Half Title
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Table of Contents
  7. Preface
  8. Editors
  9. Contributors
  10. Section I: Methods for Univariate Count Processes
    1. 1: Statistical Analysis of Count Time Series Models: A GLM Perspective (1/6)
    2. 1: Statistical Analysis of Count Time Series Models: A GLM Perspective (2/6)
    3. 1: Statistical Analysis of Count Time Series Models: A GLM Perspective (3/6)
    4. 1: Statistical Analysis of Count Time Series Models: A GLM Perspective (4/6)
    5. 1: Statistical Analysis of Count Time Series Models: A GLM Perspective (5/6)
    6. 1: Statistical Analysis of Count Time Series Models: A GLM Perspective (6/6)
    7. 2: Markov Models for Count Time Series (1/5)
    8. 2: Markov Models for Count Time Series (2/5)
    9. 2: Markov Models for Count Time Series (3/5)
    10. 2: Markov Models for Count Time Series (4/5)
    11. 2: Markov Models for Count Time Series (5/5)
    12. 3: Generalized Linear Autoregressive Moving Average Models (1/6)
    13. 3: Generalized Linear Autoregressive Moving Average Models (2/6)
    14. 3: Generalized Linear Autoregressive Moving Average Models (3/6)
    15. 3: Generalized Linear Autoregressive Moving Average Models (4/6)
    16. 3: Generalized Linear Autoregressive Moving Average Models (5/6)
    17. 3: Generalized Linear Autoregressive Moving Average Models (6/6)
    18. 4: Count Time Series with Observation-Driven Autoregressive Parameter Dynamics (1/5)
    19. 4: Count Time Series with Observation-Driven Autoregressive Parameter Dynamics (2/5)
    20. 4: Count Time Series with Observation-Driven Autoregressive Parameter Dynamics (3/5)
    21. 4: Count Time Series with Observation-Driven Autoregressive Parameter Dynamics (4/5)
    22. 4: Count Time Series with Observation-Driven Autoregressive Parameter Dynamics (5/5)
    23. 5: Renewal-Based Count Time Series (1/4)
    24. 5: Renewal-Based Count Time Series (2/4)
    25. 5: Renewal-Based Count Time Series (3/4)
    26. 5: Renewal-Based Count Time Series (4/4)
    27. 6: State Space Models for Count Time Series (1/5)
    28. 6: State Space Models for Count Time Series (2/5)
    29. 6: State Space Models for Count Time Series (3/5)
    30. 6: State Space Models for Count Time Series (4/5)
    31. 6: State Space Models for Count Time Series (5/5)
    32. 7: Estimating Equation Approaches for Integer-Valued Time Series Models (1/4)
    33. 7: Estimating Equation Approaches for Integer-Valued Time Series Models (2/4)
    34. 7: Estimating Equation Approaches for Integer-Valued Time Series Models (3/4)
    35. 7: Estimating Equation Approaches for Integer-Valued Time Series Models (4/4)
    36. 8: Dynamic Bayesian Models for Discrete-Valued Time Series (1/5)
    37. 8: Dynamic Bayesian Models for Discrete-Valued Time Series (2/5)
    38. 8: Dynamic Bayesian Models for Discrete-Valued Time Series (3/5)
    39. 8: Dynamic Bayesian Models for Discrete-Valued Time Series (4/5)
    40. 8: Dynamic Bayesian Models for Discrete-Valued Time Series (5/5)
  11. Section II: Diagnostics and Applications
    1. 9: Model Validation and Diagnostics (1/6)
    2. 9: Model Validation and Diagnostics (2/6)
    3. 9: Model Validation and Diagnostics (3/6)
    4. 9: Model Validation and Diagnostics (4/6)
    5. 9: Model Validation and Diagnostics (5/6)
    6. 9: Model Validation and Diagnostics (6/6)
    7. 10: Detection of Change Points in Discrete-Valued Time Series (1/6)
    8. 10: Detection of Change Points in Discrete-Valued Time Series (2/6)
    9. 10: Detection of Change Points in Discrete-Valued Time Series (3/6)
    10. 10: Detection of Change Points in Discrete-Valued Time Series (4/6)
    11. 10: Detection of Change Points in Discrete-Valued Time Series (5/6)
    12. 10: Detection of Change Points in Discrete-Valued Time Series (6/6)
    13. 11: Bayesian Modeling of Time Series of Counts with Business Applications (1/4)
    14. 11: Bayesian Modeling of Time Series of Counts with Business Applications (2/4)
    15. 11: Bayesian Modeling of Time Series of Counts with Business Applications (3/4)
    16. 11: Bayesian Modeling of Time Series of Counts with Business Applications (4/4)
  12. Section III: Binary and Categorical-Valued Time Series
    1. 12: Hidden Markov Models for Discrete-Valued Time Series (1/4)
    2. 12: Hidden Markov Models for Discrete-Valued Time Series (2/4)
    3. 12: Hidden Markov Models for Discrete-Valued Time Series (3/4)
    4. 12: Hidden Markov Models for Discrete-Valued Time Series (4/4)
    5. 13: Spectral Analysis of Qualitative Time Series (1/5)
    6. 13: Spectral Analysis of Qualitative Time Series (2/5)
    7. 13: Spectral Analysis of Qualitative Time Series (3/5)
    8. 13: Spectral Analysis of Qualitative Time Series (4/5)
    9. 13: Spectral Analysis of Qualitative Time Series (5/5)
    10. 14: Coherence Consideration in Binary Time Series Analysis (1/3)
    11. 14: Coherence Consideration in Binary Time Series Analysis (2/3)
    12. 14: Coherence Consideration in Binary Time Series Analysis (3/3)
  13. Section IV: Discrete-Valued Spatio-Temporal Processes
    1. 15: Hierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data (1/5)
    2. 15: Hierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data (2/5)
    3. 15: Hierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data (3/5)
    4. 15: Hierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data (4/5)
    5. 15: Hierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data (5/5)
    6. 16: Hierarchical Agent-Based Spatio-Temporal Dynamic Models for Discrete-Valued Data (1/4)
    7. 16: Hierarchical Agent-Based Spatio-Temporal Dynamic Models for Discrete-Valued Data (2/4)
    8. 16: Hierarchical Agent-Based Spatio-Temporal Dynamic Models for Discrete-Valued Data (3/4)
    9. 16: Hierarchical Agent-Based Spatio-Temporal Dynamic Models for Discrete-Valued Data (4/4)
    10. 17: Autologistic Regression Models for Spatio-Temporal Binary Data (1/4)
    11. 17: Autologistic Regression Models for Spatio-Temporal Binary Data (2/4)
    12. 17: Autologistic Regression Models for Spatio-Temporal Binary Data (3/4)
    13. 17: Autologistic Regression Models for Spatio-Temporal Binary Data (4/4)
    14. 18: Spatio-Temporal Modeling for Small Area Health Analysis (1/4)
    15. 18: Spatio-Temporal Modeling for Small Area Health Analysis (2/4)
    16. 18: Spatio-Temporal Modeling for Small Area Health Analysis (3/4)
    17. 18: Spatio-Temporal Modeling for Small Area Health Analysis (4/4)
  14. Section V: Multivariate and Long Memory Discrete-Valued Processes
    1. 19: Models for Multivariate Count Time Series (1/4)
    2. 19: Models for Multivariate Count Time Series (2/4)
    3. 19: Models for Multivariate Count Time Series (3/4)
    4. 19: Models for Multivariate Count Time Series (4/4)
    5. 20: Dynamic Models for Time Series of Counts with a Marketing Application (1/5)
    6. 20: Dynamic Models for Time Series of Counts with a Marketing Application (2/5)
    7. 20: Dynamic Models for Time Series of Counts with a Marketing Application (3/5)
    8. 20: Dynamic Models for Time Series of Counts with a Marketing Application (4/5)
    9. 20: Dynamic Models for Time Series of Counts with a Marketing Application (5/5)
    10. 21: Long Memory Discrete-Valued Time Series (1/3)
    11. 21: Long Memory Discrete-Valued Time Series (2/3)
    12. 21: Long Memory Discrete-Valued Time Series (3/3)
  15. Index (1/2)
  16. Index (2/2)