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 can be applied to other types of discrete-valued time series, such as binary-valued or categorical time series. Explore a Balanced Treatment of Frequentist and Bayesian Perspectives Accessible to graduate-level students who have taken an elementary class in statistical time series analysis, the book begins with the history and current methods for modeling and analyzing univariate count series. It next discusses diagnostics and applications before proceeding to binary and categorical time series. The book then provides a guide to modern methods for discrete-valued spatio-temporal data, illustrating how far modern applications have evolved from their roots. The book ends with a focus on multivariate and long-memory count series. Get Guidance from Masters in the Field Written by a cohesive group of distinguished contributors, this handbook provides a unified account of the diverse techniques available for observation- and parameter-driven models. It covers likelihood and approximate likelihood methods, estimating equations, simulation methods, and a Bayesian approach for model fitting.
Methods for Univariate Count Processes Statistical Analysis of Count Time Series Models: AGLM Perspective Konstantinos Fokianos Markov Models for Count Time Series Harry Joe Generalized Linear Autoregressive Moving Average Models William T.M. Dunsmuir Count Time Series with Observation-Driven Autoregressive Parameter Dynamics Dag Tjostheim Renewal-Based Count Time Series Robert Lund and James Livsey State Space Models for Count Time Series Richard A. Davis and William T.M. Dunsmuir Estimating Equation Approaches for Integer-Valued Time Series Models Aerambamoorthy Thavaneswaran and Nalini Ravishanker Dynamic Bayesian Models for Discrete-Valued Time Series Dani Gamerman, Carlos A. Abanto-Valle, Ralph S. Silva, and Thiago G. Martins Diagnostics and Applications Model Validation and Diagnostics Robert C. Jung, Brendan P.M. McCabe, and A.R. Tremayne Detection of Change Points in Discrete-Valued Time Series Claudia Kirch and Joseph Tadjuidje Kamgaing Bayesian Modeling of Time Series of Counts with Business Applications Refik Soyer, Tevfik Aktekin, and Bumsoo Kim Binary and Categorical-Valued Time Series Hidden Markov Models for Discrete-Valued Time Series Iain L. MacDonald and Walter Zucchini Spectral Analysis of Qualitative Time Series David Stoffer Coherence Consideration in Binary Time Series Analysis Benjamin Kedem Discrete-Valued Spatio-Temporal Processes Hierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data Scott H. Holan and Christopher K. Wikle Hierarchical Agent-Based Spatio-Temporal Dynamic Models for Discrete-Valued Data Christopher K. Wikle and Mevin B. Hooten Autologistic Regression Models for Spatio-Temporal Binary Data Jun Zhu and Yanbing Zheng Spatio-Temporal Modeling for Small Area Health Analysis Andrew B. Lawson and Ana Corberan-Vallet Multivariate and Long Memory Discrete-Valued Processes Models for Multivariate Count Time Series Dynamic Models for Time Series of Counts with a Marketing Application Nalini Ravishanker, Rajkumar Venkatesan, and Shan Hu Long Memory Discrete-Valued Time Series Robert Lund, Scott H. Holan, and James Livsey
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