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by Christian H. Weiss
An Introduction to Discrete-Valued Time Series
Cover
Title Page
Copyright
Dedication
Preface
About the Companion Website
Chapter 1: Introduction
Part I: Count Time Series
Chapter 2: A First Approach for Modeling Time Series of Counts: The Thinning-based INAR(1) Model
2.0 Preliminaries: Notation and Characteristics of Count Distributions
2.1 The INAR(1) Model for Time-dependent Counts
2.2 Approaches for Parameter Estimation
2.3 Model Identification
2.4 Checking for Model Adequacy
2.5 A Real-data Example
2.6 Forecasting of INAR(1) Processes
Chapter 3: Further Thinning-based Models for Count Time Series
3.1 Higher-order INARMA Models
3.2 Alternative Thinning Concepts
3.3 The Binomial AR Model
3.4 Multivariate INARMA Models
Chapter 4: INGARCH Models for Count Time Series
4.1 Poisson Autoregression
4.2 Further Types of INGARCH Models
4.3 Multivariate INGARCH Models
Chapter 5: Further Models for Count Time Series
5.1 Regression Models
5.2 Hidden-Markov Models
5.3 Discrete ARMA Models
Part II: Categorical Time Series
Chapter 6: Analyzing Categorical Time Series
6.1 Introduction to Categorical Time Series Analysis
6.2 Marginal Properties of Categorical Time Series
6.3 Serial Dependence of Categorical Time Series
Chapter 7: Models for Categorical Time Series
7.1 Parsimoniously Parametrized Markov Models
7.2 Discrete ARMA Models
7.3 Hidden-Markov Models
7.4 Regression Models
Part III: Monitoring Discrete-Valued Processes
Chapter 8: Control Charts for Count Processes
8.1 Introduction to Statistical Process Control
8.2 Shewhart Charts for Count Processes
8.3 Advanced Control Charts for Count Processes
Chapter 9: Control Charts for Categorical Processes
9.1 Sample-based Monitoring of Categorical Processes
9.2 Continuously Monitoring Categorical Processes
Part IV: Appendices
Appendix A: Examples of Count Distributions
A.1 Count Models for an Infinite Range
A.2 Count Models for a Finite Range
A.3 Multivariate Count Models
Appendix B: Basics about Stochastic Processes and Time Series
B.1 Stochastic Processes: Basic Terms and Concepts
B.2 Discrete-Valued Markov Chains
B.3 ARMA Models: Definition and Properties
B.4 Further Selected Models for Continuous-valued Time Series
Appendix C: Computational Aspects
C.1 Some Comments about the Use of R
C.2 List of R Codes
C.3 List of Datasets
References
List of Acronyms
List of Notations
Index
End User License Agreement
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An Introduction to Discrete-Valued Time Series
Christian H. Weiss
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